{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/zero-shot-generalization/papers/2","list_of":"/task/zero-shot-generalization","task":"Zero-shot Generalization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":6,"rows_per_page":100,"rows":[101,200],"of":572,"counts":{"archive_papers_tagged":572,"with_a_code_link":301,"where_syntology_ran_a_sample":128,"not_listed_spam_title":0,"listed":572,"listed_where_code_ran":128,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":119,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":119,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/zero-shot-generalization","prev":"/task/zero-shot-generalization","next":"/task/zero-shot-generalization/papers/3","papers":[{"url":"/paper/monster-marry-monodepth-to-stereo-unleashes","slug":"monster-marry-monodepth-to-stereo-unleashes","title":"MonSter: Marry Monodepth to Stereo Unleashes Power","date":"2025-01-15","arxiv_id":"2501.08643","repositories_listed":1,"syntology":null},{"url":"/paper/learning-flexible-heterogeneous-coordination","slug":"learning-flexible-heterogeneous-coordination","title":"Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination","date":"2025-01-10","arxiv_id":"2501.06058","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-flexible-heterogeneous-coordination#ran","syntology_url":"https://syntology.ai/paper/2501.06058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.06058"}},"official":{"repos":["kfu02/jaxmarl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/depth-any-camera-zero-shot-metric-depth","slug":"depth-any-camera-zero-shot-metric-depth","title":"Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera","date":"2025-01-05","arxiv_id":"2501.02464","repositories_listed":1,"syntology":{"n":12,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/depth-any-camera-zero-shot-metric-depth#ran","syntology_url":"https://syntology.ai/paper/2501.02464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.02464"}},"official":{"repos":["yuliangguo/depth_any_camera"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/fresa-feedforward-reconstruction-of-1","slug":"fresa-feedforward-reconstruction-of-1","title":"FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ow-ovd-unified-open-world-and-open-vocabulary","slug":"ow-ovd-unified-open-world-and-open-vocabulary","title":"OW-OVD: Unified Open World and Open Vocabulary Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-key-of-understanding-vision-tasks","slug":"the-key-of-understanding-vision-tasks","title":"Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization","date":"2024-12-24","arxiv_id":"2412.18525","repositories_listed":1,"syntology":null},{"url":"/paper/memorizing-sam-3d-medical-segment-anything","slug":"memorizing-sam-3d-medical-segment-anything","title":"Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer","date":"2024-12-18","arxiv_id":"2412.13908","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-diffusion-transformer-policies-with","slug":"efficient-diffusion-transformer-policies-with","title":"Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning","date":"2024-12-17","arxiv_id":"2412.12953","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-diffusion-transformer-policies-with#ran","syntology_url":"https://syntology.ai/paper/2412.12953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12953"}},"official":null}},{"url":"/paper/towards-open-vocabulary-video-semantic","slug":"towards-open-vocabulary-video-semantic","title":"Towards Open-Vocabulary Video Semantic Segmentation","date":"2024-12-12","arxiv_id":"2412.09329","repositories_listed":1,"syntology":null},{"url":"/paper/large-concept-models-language-modeling-in-a","slug":"large-concept-models-language-modeling-in-a","title":"Large Concept Models: Language Modeling in a Sentence Representation Space","date":"2024-12-11","arxiv_id":"2412.08821","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/large-concept-models-language-modeling-in-a#ran","syntology_url":"https://syntology.ai/paper/2412.08821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08821"}},"official":{"repos":["facebookresearch/large_concept_model"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sam-mamba-mamba-guided-sam-architecture-for","slug":"sam-mamba-mamba-guided-sam-architecture-for","title":"SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation","date":"2024-12-11","arxiv_id":"2412.08482","repositories_listed":1,"syntology":null},{"url":"/paper/configx-modular-configuration-for","slug":"configx-modular-configuration-for","title":"ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning","date":"2024-12-10","arxiv_id":"2412.07507","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-transfer-and-domain-adaptation-for","slug":"knowledge-transfer-and-domain-adaptation-for","title":"Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation","date":"2024-12-09","arxiv_id":"2412.06664","repositories_listed":1,"syntology":null},{"url":"/paper/stereo-anywhere-robust-zero-shot-deep-stereo","slug":"stereo-anywhere-robust-zero-shot-deep-stereo","title":"Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail","date":"2024-12-05","arxiv_id":"2412.04472","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/stereo-anywhere-robust-zero-shot-deep-stereo#ran","syntology_url":"https://syntology.ai/paper/2412.04472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.04472"}},"official":{"repos":["bartn8/stereoanywhere"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/comprompter-reconceptualized-segment-anything","slug":"comprompter-reconceptualized-segment-anything","title":"COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection","date":"2024-11-28","arxiv_id":"2411.18858","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-decoding-makes-visual-auto","slug":"collaborative-decoding-makes-visual-auto","title":"Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient","date":"2024-11-26","arxiv_id":"2411.17787","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/collaborative-decoding-makes-visual-auto#ran","syntology_url":"https://syntology.ai/paper/2411.17787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17787"}},"official":{"repos":["czg1225/code"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/vesselfm-a-foundation-model-for-universal-3d","slug":"vesselfm-a-foundation-model-for-universal-3d","title":"vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation","date":"2024-11-26","arxiv_id":"2411.17386","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/vesselfm-a-foundation-model-for-universal-3d#ran","syntology_url":"https://syntology.ai/paper/2411.17386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17386"}},"official":{"repos":["bwittmann/vesselFM"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sam-carries-the-burden-a-semi-supervised","slug":"sam-carries-the-burden-a-semi-supervised","title":"SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation","date":"2024-11-19","arxiv_id":"2411.12602","repositories_listed":1,"syntology":null},{"url":"/paper/mlan-language-based-instruction-tuning","slug":"mlan-language-based-instruction-tuning","title":"MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language Models","date":"2024-11-15","arxiv_id":"2411.10557","repositories_listed":1,"syntology":null},{"url":"/paper/workflowllm-enhancing-workflow-orchestration","slug":"workflowllm-enhancing-workflow-orchestration","title":"WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models","date":"2024-11-08","arxiv_id":"2411.05451","repositories_listed":1,"syntology":null},{"url":"/paper/enabling-adaptive-agent-training-in-open","slug":"enabling-adaptive-agent-training-in-open","title":"Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity","date":"2024-11-07","arxiv_id":"2411.04466","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/enabling-adaptive-agent-training-in-open#ran","syntology_url":"https://syntology.ai/paper/2411.04466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04466"}},"official":{"repos":["robbycostales/diva"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/object-segmentation-from-common-fate-motion","slug":"object-segmentation-from-common-fate-motion","title":"Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli","date":"2024-11-03","arxiv_id":"2411.01505","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/object-segmentation-from-common-fate-motion#ran","syntology_url":"https://syntology.ai/paper/2411.01505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01505"}},"official":{"repos":["mtangemann/motion_energy_segmentation"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/zim-zero-shot-image-matting-for-anything","slug":"zim-zero-shot-image-matting-for-anything","title":"ZIM: Zero-Shot Image Matting for Anything","date":"2024-11-01","arxiv_id":"2411.00626","repositories_listed":1,"syntology":null},{"url":"/paper/instruction-tuning-llama-3-8b-excels-in-city","slug":"instruction-tuning-llama-3-8b-excels-in-city","title":"Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction","date":"2024-10-31","arxiv_id":"2410.23692","repositories_listed":1,"syntology":null},{"url":"/paper/bigr-harnessing-binary-latent-codes-for-image","slug":"bigr-harnessing-binary-latent-codes-for-image","title":"BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities","date":"2024-10-18","arxiv_id":"2410.14672","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bigr-harnessing-binary-latent-codes-for-image#ran","syntology_url":"https://syntology.ai/paper/2410.14672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14672"}},"official":{"repos":["haoosz/BiGR"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-dt-offline-meta-rl-as-conditional","slug":"meta-dt-offline-meta-rl-as-conditional","title":"Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement","date":"2024-10-15","arxiv_id":"2410.11448","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/meta-dt-offline-meta-rl-as-conditional#ran","syntology_url":"https://syntology.ai/paper/2410.11448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11448"}},"official":{"repos":["nju-rl/meta-dt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mote-reconciling-generalization-with","slug":"mote-reconciling-generalization-with","title":"MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer","date":"2024-10-14","arxiv_id":"2410.10589","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":1,"n_ran_checked":4,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mote-reconciling-generalization-with#ran","syntology_url":"https://syntology.ai/paper/2410.10589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10589"}},"official":{"repos":["zmhh-h/mote"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rdt-1b-a-diffusion-foundation-model-for","slug":"rdt-1b-a-diffusion-foundation-model-for","title":"RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation","date":"2024-10-10","arxiv_id":"2410.07864","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rdt-1b-a-diffusion-foundation-model-for#ran","syntology_url":"https://syntology.ai/paper/2410.07864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07864"}},"official":{"repos":["thu-ml/RoboticsDiffusionTransformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-fact-verification-via-natural-logic","slug":"zero-shot-fact-verification-via-natural-logic","title":"Zero-Shot Fact Verification via Natural Logic and Large Language Models","date":"2024-10-04","arxiv_id":"2410.03341","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":10,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-fact-verification-via-natural-logic#ran","syntology_url":"https://syntology.ai/paper/2410.03341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03341"}},"official":{"repos":["marekstrong/Zero-NatVer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/medvilam-a-multimodal-large-language-model","slug":"medvilam-a-multimodal-large-language-model","title":"MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation","date":"2024-09-29","arxiv_id":"2409.19684","repositories_listed":1,"syntology":null},{"url":"/paper/lotus-diffusion-based-visual-foundation-model","slug":"lotus-diffusion-based-visual-foundation-model","title":"Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction","date":"2024-09-26","arxiv_id":"2409.18124","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lotus-diffusion-based-visual-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2409.18124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18124"}},"official":null}},{"url":"/paper/a-novel-open-source-ultrasound-dataset-with","slug":"a-novel-open-source-ultrasound-dataset-with","title":"A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation","date":"2024-09-24","arxiv_id":"2409.16441","repositories_listed":1,"syntology":null},{"url":"/paper/scaleflow-robust-and-accurate-estimation-of","slug":"scaleflow-robust-and-accurate-estimation-of","title":"ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video","date":"2024-09-16","arxiv_id":"2409.12202","repositories_listed":1,"syntology":null},{"url":"/paper/primedepth-efficient-monocular-depth","slug":"primedepth-efficient-monocular-depth","title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","date":"2024-09-13","arxiv_id":"2409.09144","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":1,"n_honours":2,"n_violates":3,"n_no_contract":5,"n_pointer_only":6,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 3 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/primedepth-efficient-monocular-depth#ran","syntology_url":"https://syntology.ai/paper/2409.09144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09144"}},"official":{"repos":["vislearn/PrimeDepth"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/indicvoices-r-unlocking-a-massive","slug":"indicvoices-r-unlocking-a-massive","title":"IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS","date":"2024-09-09","arxiv_id":"2409.05356","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/indicvoices-r-unlocking-a-massive#ran","syntology_url":"https://syntology.ai/paper/2409.05356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.05356"}},"official":{"repos":["ai4bharat/indicvoices-r"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adapting-segment-anything-model-to-multi","slug":"adapting-segment-anything-model-to-multi","title":"Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance","date":"2024-08-27","arxiv_id":"2408.15063","repositories_listed":1,"syntology":null},{"url":"/paper/gr-mg-leveraging-partially-annotated-data-via","slug":"gr-mg-leveraging-partially-annotated-data-via","title":"GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy","date":"2024-08-26","arxiv_id":"2408.14368","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gr-mg-leveraging-partially-annotated-data-via#ran","syntology_url":"https://syntology.ai/paper/2408.14368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.14368"}},"official":{"repos":["bytedance/GR-MG"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generalizable-facial-expression-recognition","slug":"generalizable-facial-expression-recognition","title":"Generalizable Facial Expression Recognition","date":"2024-08-20","arxiv_id":"2408.10614","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generalizable-facial-expression-recognition#ran","syntology_url":"https://syntology.ai/paper/2408.10614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10614"}},"official":{"repos":["zyh-uaiaaaa/generalizable-fer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/opencity-open-spatio-temporal-foundation","slug":"opencity-open-spatio-temporal-foundation","title":"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction","date":"2024-08-16","arxiv_id":"2408.10269","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opencity-open-spatio-temporal-foundation#ran","syntology_url":"https://syntology.ai/paper/2408.10269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10269"}},"official":{"repos":["hkuds/opencity"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/one-shot-is-enough-for-sequential-infrared","slug":"one-shot-is-enough-for-sequential-infrared","title":"One Shot is Enough for Sequential Infrared Small Target Segmentation","date":"2024-08-09","arxiv_id":"2408.04823","repositories_listed":1,"syntology":null},{"url":"/paper/2408-01942","slug":"2408-01942","title":"Visual Grounding for Object-Level Generalization in Reinforcement Learning","date":"2024-08-04","arxiv_id":"2408.01942","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/2408-01942#ran","syntology_url":"https://syntology.ai/paper/2408.01942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.01942"}},"official":{"repos":["pku-rl/copl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/2408-01230","slug":"2408-01230","title":"HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling","date":"2024-08-02","arxiv_id":"2408.01230","repositories_listed":1,"syntology":null},{"url":"/paper/hybriddepth-robust-depth-fusion-for-mobile-ar","slug":"hybriddepth-robust-depth-fusion-for-mobile-ar","title":"HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors","date":"2024-07-26","arxiv_id":"2407.18443","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hybriddepth-robust-depth-fusion-for-mobile-ar#ran","syntology_url":"https://syntology.ai/paper/2407.18443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18443"}},"official":{"repos":["cake-lab/hybriddepth"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/test-time-low-rank-adaptation-via-confidence","slug":"test-time-low-rank-adaptation-via-confidence","title":"Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models","date":"2024-07-22","arxiv_id":"2407.15913","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/test-time-low-rank-adaptation-via-confidence#ran","syntology_url":"https://syntology.ai/paper/2407.15913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15913"}},"official":{"repos":["razaimam45/ttl-test-time-low-rank-adaptation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/scaleraft-cross-scale-recurrent-all-pairs","slug":"scaleraft-cross-scale-recurrent-all-pairs","title":"ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video","date":"2024-07-13","arxiv_id":"2407.09797","repositories_listed":1,"syntology":null},{"url":"/paper/swiss-dino-efficient-and-versatile-vision","slug":"swiss-dino-efficient-and-versatile-vision","title":"Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search","date":"2024-07-10","arxiv_id":"2407.07541","repositories_listed":1,"syntology":null},{"url":"/paper/unified-embedding-alignment-for-open","slug":"unified-embedding-alignment-for-open","title":"Unified Embedding Alignment for Open-Vocabulary Video Instance Segmentation","date":"2024-07-10","arxiv_id":"2407.07427","repositories_listed":1,"syntology":null},{"url":"/paper/improving-zero-shot-generalization-of-learned","slug":"improving-zero-shot-generalization-of-learned","title":"Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation","date":"2024-07-03","arxiv_id":"2407.03056","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-zero-shot-generalization-of-learned#ran","syntology_url":"https://syntology.ai/paper/2407.03056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03056"}},"official":{"repos":["miccunifi/kdpl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-two-stage-reinforcement-learning-based","slug":"a-two-stage-reinforcement-learning-based","title":"A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation","date":"2024-06-29","arxiv_id":"2407.00496","repositories_listed":1,"syntology":null},{"url":"/paper/robouniview-visual-language-model-with","slug":"robouniview-visual-language-model-with","title":"RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation","date":"2024-06-27","arxiv_id":"2406.18977","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robouniview-visual-language-model-with#ran","syntology_url":"https://syntology.ai/paper/2406.18977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18977"}},"official":{"repos":["liufanfanlff/robouniview"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/geobench-benchmarking-and-analyzing-monocular","slug":"geobench-benchmarking-and-analyzing-monocular","title":"GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models","date":"2024-06-18","arxiv_id":"2406.12671","repositories_listed":1,"syntology":null},{"url":"/paper/words-in-motion-representation-engineering","slug":"words-in-motion-representation-engineering","title":"Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers","date":"2024-06-17","arxiv_id":"2406.11624","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/words-in-motion-representation-engineering#ran","syntology_url":"https://syntology.ai/paper/2406.11624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11624"}},"official":{"repos":["kit-mrt/future-motion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-exploration-of-cross-lingual-zero-shot","slug":"deep-exploration-of-cross-lingual-zero-shot","title":"Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning","date":"2024-06-13","arxiv_id":"2406.08796","repositories_listed":1,"syntology":null},{"url":"/paper/robustsam-segment-anything-robustly-on-1","slug":"robustsam-segment-anything-robustly-on-1","title":"RobustSAM: Segment Anything Robustly on Degraded Images","date":"2024-06-13","arxiv_id":"2406.09627","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/robustsam-segment-anything-robustly-on-1#ran","syntology_url":"https://syntology.ai/paper/2406.09627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09627"}},"official":{"repos":["robustsam/RobustSAM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-text-cross-alignment-refining-the","slug":"visual-text-cross-alignment-refining-the","title":"Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models","date":"2024-06-05","arxiv_id":"2406.02915","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":0,"n_instrument":8,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 8 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/visual-text-cross-alignment-refining-the#ran","syntology_url":"https://syntology.ai/paper/2406.02915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02915"}},"official":{"repos":["tmlr-group/wca"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/olive-object-level-in-context-visual","slug":"olive-object-level-in-context-visual","title":"OLIVE: Object Level In-Context Visual Embeddings","date":"2024-06-02","arxiv_id":"2406.00872","repositories_listed":1,"syntology":null},{"url":"/paper/m-lo-compute-efficient-meta-generalization-of","slug":"m-lo-compute-efficient-meta-generalization-of","title":"$μ$LO: Compute-Efficient Meta-Generalization of Learned Optimizers","date":"2024-05-31","arxiv_id":"2406.00153","repositories_listed":1,"syntology":null},{"url":"/paper/m-3-gpt-an-advanced-multimodal-multitask","slug":"m-3-gpt-an-advanced-multimodal-multitask","title":"M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation","date":"2024-05-25","arxiv_id":"2405.16273","repositories_listed":1,"syntology":{"n":25,"n_ran":17,"n_constructed":4,"n_ran_checked":15,"n_instrument":2,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":25,"phrase":"17 ran (of which 4 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/m-3-gpt-an-advanced-multimodal-multitask#ran","syntology_url":"https://syntology.ai/paper/2405.16273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16273"}},"official":{"repos":["luomingshuang/m3gpt"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":4,"n_ran_no_instrument_failure":15,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/smart-scalable-multi-agent-real-time","slug":"smart-scalable-multi-agent-real-time","title":"SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction","date":"2024-05-24","arxiv_id":"2405.15677","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/smart-scalable-multi-agent-real-time#ran","syntology_url":"https://syntology.ai/paper/2405.15677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15677"}},"official":{"repos":["rainmaker22/SMART"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/prompt-learning-for-generalized-vehicle","slug":"prompt-learning-for-generalized-vehicle","title":"Prompt Learning for Generalized Vehicle Routing","date":"2024-05-20","arxiv_id":"2405.12262","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/prompt-learning-for-generalized-vehicle#ran","syntology_url":"https://syntology.ai/paper/2405.12262","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.12262"}},"official":{"repos":["feiliu36/promptvrp"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/on-the-test-time-zero-shot-generalization-of","slug":"on-the-test-time-zero-shot-generalization-of","title":"On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?","date":"2024-05-03","arxiv_id":"2405.02266","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-test-time-zero-shot-generalization-of#ran","syntology_url":"https://syntology.ai/paper/2405.02266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.02266"}},"official":{"repos":["maxzanella/mta"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instruction-matters-a-simple-yet-effective","slug":"instruction-matters-a-simple-yet-effective","title":"Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks","date":"2024-04-25","arxiv_id":"2404.16418","repositories_listed":1,"syntology":null},{"url":"/paper/supercompiler-code-optimization-with-zero","slug":"supercompiler-code-optimization-with-zero","title":"CompilerDream: Learning a Compiler World Model for General Code Optimization","date":"2024-04-24","arxiv_id":"2404.16077","repositories_listed":1,"syntology":null},{"url":"/paper/inferring-behavior-specific-context-improves","slug":"inferring-behavior-specific-context-improves","title":"Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning","date":"2024-04-15","arxiv_id":"2404.09521","repositories_listed":1,"syntology":null},{"url":"/paper/clip-embed-kd-computationally-efficient","slug":"clip-embed-kd-computationally-efficient","title":"CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers","date":"2024-04-09","arxiv_id":"2404.06170","repositories_listed":1,"syntology":null},{"url":"/paper/geosynth-contextually-aware-high-resolution","slug":"geosynth-contextually-aware-high-resolution","title":"GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis","date":"2024-04-09","arxiv_id":"2404.06637","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geosynth-contextually-aware-high-resolution#ran","syntology_url":"https://syntology.ai/paper/2404.06637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06637"}},"official":{"repos":["mvrl/geosynth"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/text-based-reasoning-about-vector-graphics","slug":"text-based-reasoning-about-vector-graphics","title":"Visually Descriptive Language Model for Vector Graphics Reasoning","date":"2024-04-09","arxiv_id":"2404.06479","repositories_listed":1,"syntology":null},{"url":"/paper/know-your-neighbors-improving-single-view","slug":"know-your-neighbors-improving-single-view","title":"Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning","date":"2024-04-04","arxiv_id":"2404.03658","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/know-your-neighbors-improving-single-view#ran","syntology_url":"https://syntology.ai/paper/2404.03658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03658"}},"official":{"repos":["ruili3/Know-Your-Neighbors"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/no-zero-shot-without-exponential-data","slug":"no-zero-shot-without-exponential-data","title":"No \"Zero-Shot\" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance","date":"2024-04-04","arxiv_id":"2404.04125","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/no-zero-shot-without-exponential-data#ran","syntology_url":"https://syntology.ai/paper/2404.04125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04125"}},"official":{"repos":["bethgelab/frequency_determines_performance"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/where-to-move-next-zero-shot-generalization","slug":"where-to-move-next-zero-shot-generalization","title":"Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation","date":"2024-04-02","arxiv_id":"2404.01855","repositories_listed":1,"syntology":null},{"url":"/paper/metric3d-v2-a-versatile-monocular-geometric-1","slug":"metric3d-v2-a-versatile-monocular-geometric-1","title":"Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation","date":"2024-03-22","arxiv_id":"2404.15506","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metric3d-v2-a-versatile-monocular-geometric-1#ran","syntology_url":"https://syntology.ai/paper/2404.15506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.15506"}},"official":{"repos":["yvanyin/metric3d"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/just-shift-it-test-time-prototype-shifting","slug":"just-shift-it-test-time-prototype-shifting","title":"Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models","date":"2024-03-19","arxiv_id":"2403.12952","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/just-shift-it-test-time-prototype-shifting#ran","syntology_url":"https://syntology.ai/paper/2403.12952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12952"}},"official":{"repos":["elaine-sui/tps"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/data-efficient-contrastive-language-image","slug":"data-efficient-contrastive-language-image","title":"Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity","date":"2024-03-18","arxiv_id":"2403.12267","repositories_listed":1,"syntology":null},{"url":"/paper/selective-hourglass-mapping-for-universal","slug":"selective-hourglass-mapping-for-universal","title":"Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model","date":"2024-03-17","arxiv_id":"2403.11157","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/selective-hourglass-mapping-for-universal#ran","syntology_url":"https://syntology.ai/paper/2403.11157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11157"}},"official":{"repos":["isee-laboratory/diffuir"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dreaming-of-many-worlds-learning-contextual","slug":"dreaming-of-many-worlds-learning-contextual","title":"Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization","date":"2024-03-16","arxiv_id":"2403.10967","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dreaming-of-many-worlds-learning-contextual#ran","syntology_url":"https://syntology.ai/paper/2403.10967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10967"}},"official":{"repos":["sai-prasanna/dreaming_of_many_worlds"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fluorosam-a-language-aligned-foundation-model","slug":"fluorosam-a-language-aligned-foundation-model","title":"FluoroSAM: A Language-aligned Foundation Model for X-ray Image Segmentation","date":"2024-03-12","arxiv_id":"2403.08059","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-surgical-instrument-segmentation-in","slug":"real-time-surgical-instrument-segmentation-in","title":"Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything","date":"2024-03-12","arxiv_id":"2403.08003","repositories_listed":1,"syntology":null},{"url":"/paper/sam-pd-how-far-can-sam-take-us-in-tracking","slug":"sam-pd-how-far-can-sam-take-us-in-tracking","title":"SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising","date":"2024-03-07","arxiv_id":"2403.04194","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-generalizable-incremental-learning","slug":"zero-shot-generalizable-incremental-learning","title":"Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection","date":"2024-03-04","arxiv_id":"2403.01680","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-generalizable-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/2403.01680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01680"}},"official":{"repos":["jarintotiondin/ziragroundingdino"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kick-back-relax-scaling-beyond-ground-truth","slug":"kick-back-relax-scaling-beyond-ground-truth","title":"Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV","date":"2024-03-03","arxiv_id":"2403.01569","repositories_listed":1,"syntology":null},{"url":"/paper/segment-anything-model-for-head-and-neck","slug":"segment-anything-model-for-head-and-neck","title":"Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images","date":"2024-02-27","arxiv_id":"2402.17454","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-instruction-tuning-with","slug":"multimodal-instruction-tuning-with","title":"Multimodal Instruction Tuning with Conditional Mixture of LoRA","date":"2024-02-24","arxiv_id":"2402.15896","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/multimodal-instruction-tuning-with#ran","syntology_url":"https://syntology.ai/paper/2402.15896","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15896"}},"official":{"repos":["vt-nlp/mixlora"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-learning-for-routing-problem-with","slug":"multi-task-learning-for-routing-problem-with","title":"Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization","date":"2024-02-23","arxiv_id":"2402.16891","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-task-learning-for-routing-problem-with#ran","syntology_url":"https://syntology.ai/paper/2402.16891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16891"}},"official":{"repos":["feiliu36/mtnco"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/iepile-unearthing-large-scale-schema-based","slug":"iepile-unearthing-large-scale-schema-based","title":"IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus","date":"2024-02-22","arxiv_id":"2402.14710","repositories_listed":1,"syntology":null},{"url":"/paper/triple-encoders-representations-that-fire","slug":"triple-encoders-representations-that-fire","title":"Triple-Encoders: Representations That Fire Together, Wire Together","date":"2024-02-19","arxiv_id":"2402.12332","repositories_listed":1,"syntology":null},{"url":"/paper/3d-diffuser-actor-policy-diffusion-with-3d","slug":"3d-diffuser-actor-policy-diffusion-with-3d","title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","date":"2024-02-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/distilling-morphology-conditioned","slug":"distilling-morphology-conditioned","title":"Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control","date":"2024-02-09","arxiv_id":"2402.06570","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/distilling-morphology-conditioned#ran","syntology_url":"https://syntology.ai/paper/2402.06570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06570"}},"official":{"repos":["masterxiong/universal-morphology-control"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tag-llm-repurposing-general-purpose-llms-for","slug":"tag-llm-repurposing-general-purpose-llms-for","title":"Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains","date":"2024-02-06","arxiv_id":"2402.05140","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":2,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/tag-llm-repurposing-general-purpose-llms-for#ran","syntology_url":"https://syntology.ai/paper/2402.05140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05140"}},"official":{"repos":["sjunhongshen/tag-llm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/image-caption-encoding-for-improving-zero","slug":"image-caption-encoding-for-improving-zero","title":"Image-Caption Encoding for Improving Zero-Shot Generalization","date":"2024-02-05","arxiv_id":"2402.02662","repositories_listed":1,"syntology":null},{"url":"/paper/symbol-generating-flexible-black-box","slug":"symbol-generating-flexible-black-box","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","date":"2024-02-04","arxiv_id":"2402.02355","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":1,"n_ran_checked":14,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"15 ran (of which 1 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/symbol-generating-flexible-black-box#ran","syntology_url":"https://syntology.ai/paper/2402.02355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02355"}},"official":{"repos":["gmc-drl/symbol"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":1,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/convolution-meets-lora-parameter-efficient","slug":"convolution-meets-lora-parameter-efficient","title":"Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model","date":"2024-01-31","arxiv_id":"2401.17868","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convolution-meets-lora-parameter-efficient#ran","syntology_url":"https://syntology.ai/paper/2401.17868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17868"}},"official":{"repos":["autogluon/autogluon"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data-free-generalized-zero-shot-learning","slug":"data-free-generalized-zero-shot-learning","title":"Data-Free Generalized Zero-Shot Learning","date":"2024-01-28","arxiv_id":"2401.15657","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":14,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/data-free-generalized-zero-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2401.15657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15657"}},"official":{"repos":["ylong4/dfzsl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/instructdoc-a-dataset-for-zero-shot","slug":"instructdoc-a-dataset-for-zero-shot","title":"InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions","date":"2024-01-24","arxiv_id":"2401.13313","repositories_listed":1,"syntology":null},{"url":"/paper/mugi-enhancing-information-retrieval-through","slug":"mugi-enhancing-information-retrieval-through","title":"Exploring the Best Practices of Query Expansion with Large Language Models","date":"2024-01-12","arxiv_id":"2401.06311","repositories_listed":1,"syntology":null},{"url":"/paper/matsam-efficient-materials-microstructure","slug":"matsam-efficient-materials-microstructure","title":"MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model","date":"2024-01-11","arxiv_id":"2401.05638","repositories_listed":1,"syntology":null},{"url":"/paper/pre-trained-model-guided-fine-tuning-for-zero","slug":"pre-trained-model-guided-fine-tuning-for-zero","title":"Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness","date":"2024-01-09","arxiv_id":"2401.04350","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pre-trained-model-guided-fine-tuning-for-zero#ran","syntology_url":"https://syntology.ai/paper/2401.04350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04350"}},"official":{"repos":["serendipity1122/pre-trained-model-guided-fine-tuning-for-zero-shot-adversarial-robustness"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-guidance-tuning-for-text-to-image","slug":"semantic-guidance-tuning-for-text-to-image","title":"Semantic Guidance Tuning for Text-To-Image Diffusion Models","date":"2023-12-26","arxiv_id":"2312.15964","repositories_listed":1,"syntology":null},{"url":"/paper/general-object-foundation-model-for-images","slug":"general-object-foundation-model-for-images","title":"General Object Foundation Model for Images and Videos at Scale","date":"2023-12-14","arxiv_id":"2312.09158","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/general-object-foundation-model-for-images#ran","syntology_url":"https://syntology.ai/paper/2312.09158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09158"}},"official":{"repos":["FoundationVision/GLEE"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/how-well-does-gpt-4v-ision-adapt-to","slug":"how-well-does-gpt-4v-ision-adapt-to","title":"How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation","date":"2023-12-12","arxiv_id":"2312.07424","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-well-does-gpt-4v-ision-adapt-to#ran","syntology_url":"https://syntology.ai/paper/2312.07424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07424"}},"official":{"repos":["jameszhou-gl/gpt-4v-distribution-shift"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-models-are-good-prompt","slug":"large-language-models-are-good-prompt","title":"Large Language Models are Good Prompt Learners for Low-Shot Image Classification","date":"2023-12-07","arxiv_id":"2312.04076","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/large-language-models-are-good-prompt#ran","syntology_url":"https://syntology.ai/paper/2312.04076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04076"}},"official":{"repos":["zhaohengz/llamp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"6aa6ffd22c93edb55db9604e22bd1fc59c0aa0c43953d623f566806d834aaa09","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}