{"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-learning/papers/3","list_of":"/task/zero-shot-learning","task":"Zero-Shot Learning","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":3,"pages_in_order":19,"rows_per_page":100,"rows":[201,300],"of":1864,"counts":{"archive_papers_tagged":1864,"with_a_code_link":787,"where_syntology_ran_a_sample":245,"not_listed_spam_title":0,"listed":1864,"listed_where_code_ran":245,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":204,"every_run_a_failure_of_syntologys_instrument":41,"listed_with_a_run_with_no_instrument_failure":204,"listed_every_run_a_failure_of_syntologys_instrument":41,"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-learning","prev":"/task/zero-shot-learning/papers/2","next":"/task/zero-shot-learning/papers/4","papers":[{"url":"/paper/perturb-and-recover-fine-tuning-for-effective","slug":"perturb-and-recover-fine-tuning-for-effective","title":"Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP","date":"2024-12-01","arxiv_id":"2412.00727","repositories_listed":1,"syntology":null},{"url":"/paper/clip-meets-dino-for-tuning-zero-shot","slug":"clip-meets-dino-for-tuning-zero-shot","title":"CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections","date":"2024-11-28","arxiv_id":"2411.19346","repositories_listed":1,"syntology":null},{"url":"/paper/tabletime-reformulating-time-series","slug":"tabletime-reformulating-time-series","title":"TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models","date":"2024-11-24","arxiv_id":"2411.15737","repositories_listed":1,"syntology":null},{"url":"/paper/cliper-hierarchically-improving-spatial","slug":"cliper-hierarchically-improving-spatial","title":"CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation","date":"2024-11-21","arxiv_id":"2411.13836","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-mllm-embeddings-and-attribute","slug":"leveraging-mllm-embeddings-and-attribute","title":"Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning","date":"2024-11-18","arxiv_id":"2411.12584","repositories_listed":1,"syntology":null},{"url":"/paper/visual-semantic-graph-matching-net-for-zero","slug":"visual-semantic-graph-matching-net-for-zero","title":"Visual-Semantic Graph Matching Net for Zero-Shot Learning","date":"2024-11-18","arxiv_id":"2411.11351","repositories_listed":1,"syntology":null},{"url":"/paper/tdsm-triplet-diffusion-for-skeleton-text","slug":"tdsm-triplet-diffusion-for-skeleton-text","title":"TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition","date":"2024-11-16","arxiv_id":"2411.10745","repositories_listed":1,"syntology":null},{"url":"/paper/corrclip-reconstructing-correlations-in-clip","slug":"corrclip-reconstructing-correlations-in-clip","title":"CorrCLIP: Reconstructing Correlations in CLIP with Off-the-Shelf Foundation Models for Open-Vocabulary Semantic Segmentation","date":"2024-11-15","arxiv_id":"2411.10086","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"5 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/corrclip-reconstructing-correlations-in-clip#ran","syntology_url":"https://syntology.ai/paper/2411.10086","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.10086"}},"official":{"repos":["zdk258/CorrCLIP"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-visual-classification-using","slug":"enhancing-visual-classification-using","title":"Enhancing Visual Classification using Comparative Descriptors","date":"2024-11-08","arxiv_id":"2411.05357","repositories_listed":1,"syntology":null},{"url":"/paper/ravl-discovering-and-mitigating-spurious","slug":"ravl-discovering-and-mitigating-spurious","title":"RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models","date":"2024-11-06","arxiv_id":"2411.04097","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ravl-discovering-and-mitigating-spurious#ran","syntology_url":"https://syntology.ai/paper/2411.04097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.04097"}},"official":{"repos":["stanford-aimi/ravl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/abstracted-shapes-as-tokens-a-generalizable","slug":"abstracted-shapes-as-tokens-a-generalizable","title":"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification","date":"2024-11-01","arxiv_id":"2411.01006","repositories_listed":1,"syntology":{"n":18,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"12 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; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/abstracted-shapes-as-tokens-a-generalizable#ran","syntology_url":"https://syntology.ai/paper/2411.01006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01006"}},"official":{"repos":["yunshiwen/vqshape"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/more-multi-modal-contrastive-pre-training","slug":"more-multi-modal-contrastive-pre-training","title":"MoRE: Multi-Modal Contrastive Pre-training with Transformers on X-Rays, ECGs, and Diagnostic Report","date":"2024-10-21","arxiv_id":"2410.16239","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-and-analyzing-clip-s-zero-shot","slug":"interpreting-and-analyzing-clip-s-zero-shot","title":"Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge","date":"2024-10-16","arxiv_id":"2410.13016","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":2,"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/interpreting-and-analyzing-clip-s-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2410.13016","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13016"}},"official":{"repos":["fawazsammani/clip-interpret-mutual-knowledge"],"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/llm-chain-ensembles-for-scalable-and-accurate","slug":"llm-chain-ensembles-for-scalable-and-accurate","title":"LLM Chain Ensembles for Scalable and Accurate Data Annotation","date":"2024-10-16","arxiv_id":"2410.13006","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-debiasing-approach-for-vision","slug":"a-unified-debiasing-approach-for-vision","title":"A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks","date":"2024-10-10","arxiv_id":"2410.07593","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/a-unified-debiasing-approach-for-vision#ran","syntology_url":"https://syntology.ai/paper/2410.07593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07593"}},"official":{"repos":["HoinJung/Unified-Debiaisng-VLM-SFID"],"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/glov-guided-large-language-models-as-implicit","slug":"glov-guided-large-language-models-as-implicit","title":"GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models","date":"2024-10-08","arxiv_id":"2410.06154","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/glov-guided-large-language-models-as-implicit#ran","syntology_url":"https://syntology.ai/paper/2410.06154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06154"}},"official":{"repos":["jmiemirza/glov"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/agriclip-adapting-clip-for-agriculture-and","slug":"agriclip-adapting-clip-for-agriculture-and","title":"AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment","date":"2024-10-02","arxiv_id":"2410.01407","repositories_listed":1,"syntology":null},{"url":"/paper/toward-zero-shot-learning-for-visual-dehazing","slug":"toward-zero-shot-learning-for-visual-dehazing","title":"Toward Zero-Shot Learning for Visual Dehazing of Urological Surgical Robots","date":"2024-10-02","arxiv_id":"2410.01395","repositories_listed":1,"syntology":null},{"url":"/paper/necomimi-neural-cognitive-multimodal-eeg","slug":"necomimi-neural-cognitive-multimodal-eeg","title":"NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models","date":"2024-10-01","arxiv_id":"2410.00712","repositories_listed":1,"syntology":null},{"url":"/paper/from-unimodal-to-multimodal-scaling-up","slug":"from-unimodal-to-multimodal-scaling-up","title":"From Unimodal to Multimodal: Scaling up Projectors to Align Modalities","date":"2024-09-28","arxiv_id":"2409.19425","repositories_listed":1,"syntology":null},{"url":"/paper/neuropath-a-neural-pathway-transformer-for","slug":"neuropath-a-neural-pathway-transformer-for","title":"NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes","date":"2024-09-26","arxiv_id":"2409.17510","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neuropath-a-neural-pathway-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2409.17510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17510"}},"official":{"repos":["Chrisa142857/neuro_detour"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pruning-multilingual-large-language-models","slug":"pruning-multilingual-large-language-models","title":"Pruning Multilingual Large Language Models for Multilingual Inference","date":"2024-09-25","arxiv_id":"2409.16911","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-forecasting-of-chaotic-systems","slug":"zero-shot-forecasting-of-chaotic-systems","title":"Zero-shot forecasting of chaotic systems","date":"2024-09-24","arxiv_id":"2409.15771","repositories_listed":1,"syntology":{"n":20,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/zero-shot-forecasting-of-chaotic-systems#ran","syntology_url":"https://syntology.ai/paper/2409.15771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.15771"}},"official":{"repos":["williamgilpin/dysts"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/for-overall-nighttime-visibility-integrate","slug":"for-overall-nighttime-visibility-integrate","title":"For Overall Nighttime Visibility: Integrate Irregular Glow Removal With Glow-Aware Enhancement","date":"2024-09-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-agricultural-environment-perception","slug":"enhancing-agricultural-environment-perception","title":"Enhancing Agricultural Environment Perception via Active Vision and Zero-Shot Learning","date":"2024-09-19","arxiv_id":"2409.12602","repositories_listed":1,"syntology":null},{"url":"/paper/can-large-language-models-grasp-event-signals","slug":"can-large-language-models-grasp-event-signals","title":"Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition","date":"2024-09-15","arxiv_id":"2409.09628","repositories_listed":1,"syntology":null},{"url":"/paper/analysis-of-socially-unacceptable-discourse","slug":"analysis-of-socially-unacceptable-discourse","title":"Analysis of Socially Unacceptable Discourse with Zero-shot Learning","date":"2024-09-10","arxiv_id":"2409.13735","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-clip-models-for-image-retrieval","slug":"optimizing-clip-models-for-image-retrieval","title":"Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment","date":"2024-09-03","arxiv_id":"2409.01936","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":3,"n_no_contract":8,"n_pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 3 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/optimizing-clip-models-for-image-retrieval#ran","syntology_url":"https://syntology.ai/paper/2409.01936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01936"}},"official":{"repos":["Visual-Computing/MCIP"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-remote-sensing-vision-language","slug":"enhancing-remote-sensing-vision-language","title":"Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification","date":"2024-09-01","arxiv_id":"2409.00698","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-vision-language-models-to-open","slug":"adapting-vision-language-models-to-open","title":"Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning","date":"2024-08-29","arxiv_id":"2408.16486","repositories_listed":1,"syntology":null},{"url":"/paper/mapf-gpt-imitation-learning-for-multi-agent-1","slug":"mapf-gpt-imitation-learning-for-multi-agent-1","title":"MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale","date":"2024-08-29","arxiv_id":"2409.00134","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/mapf-gpt-imitation-learning-for-multi-agent-1#ran","syntology_url":"https://syntology.ai/paper/2409.00134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00134"}},"official":{"repos":["cognitiveaisystems/mapf-gpt"],"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/single-image-denoising-via-a-new-lightweight","slug":"single-image-denoising-via-a-new-lightweight","title":"Single Image Denoising via a New Lightweight Learning-Based Model","date":"2024-08-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/online-zero-shot-classification-with-clip","slug":"online-zero-shot-classification-with-clip","title":"Online Zero-Shot Classification with CLIP","date":"2024-08-23","arxiv_id":"2408.13320","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/online-zero-shot-classification-with-clip#ran","syntology_url":"https://syntology.ai/paper/2408.13320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.13320"}},"official":{"repos":["idstcv/onzeta"],"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/fidavl-fake-image-detection-and-attribution","slug":"fidavl-fake-image-detection-and-attribution","title":"FIDAVL: Fake Image Detection and Attribution using Vision-Language Model","date":"2024-08-22","arxiv_id":"2409.03109","repositories_listed":1,"syntology":null},{"url":"/paper/xdt-cxr-investigating-cross-disease","slug":"xdt-cxr-investigating-cross-disease","title":"XDT-CXR: Investigating Cross-Disease Transferability in Zero-Shot Binary Classification of Chest X-Rays","date":"2024-08-21","arxiv_id":"2408.11493","repositories_listed":1,"syntology":null},{"url":"/paper/anygraph-graph-foundation-model-in-the-wild","slug":"anygraph-graph-foundation-model-in-the-wild","title":"AnyGraph: Graph Foundation Model in the Wild","date":"2024-08-20","arxiv_id":"2408.10700","repositories_listed":1,"syntology":null},{"url":"/paper/crossfi-a-cross-domain-wi-fi-sensing","slug":"crossfi-a-cross-domain-wi-fi-sensing","title":"CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network","date":"2024-08-20","arxiv_id":"2408.10919","repositories_listed":1,"syntology":null},{"url":"/paper/easyrec-simple-yet-effective-language-models","slug":"easyrec-simple-yet-effective-language-models","title":"EasyRec: Simple yet Effective Language Models for Recommendation","date":"2024-08-16","arxiv_id":"2408.08821","repositories_listed":1,"syntology":null},{"url":"/paper/navigating-data-scarcity-using-foundation","slug":"navigating-data-scarcity-using-foundation","title":"Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging","date":"2024-08-15","arxiv_id":"2408.08058","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-and-key-points-are-all-you","slug":"zero-shot-learning-and-key-points-are-all-you","title":"Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking","date":"2024-08-15","arxiv_id":"2408.08400","repositories_listed":1,"syntology":null},{"url":"/paper/dc3do-diffusion-classifier-for-3d-objects","slug":"dc3do-diffusion-classifier-for-3d-objects","title":"DC3DO: Diffusion Classifier for 3D Objects","date":"2024-08-13","arxiv_id":"2408.06693","repositories_listed":1,"syntology":null},{"url":"/paper/omniclip-adapting-clip-for-video-recognition","slug":"omniclip-adapting-clip-for-video-recognition","title":"OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning","date":"2024-08-12","arxiv_id":"2408.06158","repositories_listed":1,"syntology":null},{"url":"/paper/a-training-free-framework-for-video-license","slug":"a-training-free-framework-for-video-license","title":"A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot","date":"2024-08-11","arxiv_id":"2408.05729","repositories_listed":1,"syntology":null},{"url":"/paper/on-zero-shot-learning-in-neural-state","slug":"on-zero-shot-learning-in-neural-state","title":"On zero-shot learning in neural state estimation of power distribution systems","date":"2024-08-11","arxiv_id":"2408.05787","repositories_listed":1,"syntology":null},{"url":"/paper/2408-02001","slug":"2408-02001","title":"AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis","date":"2024-08-04","arxiv_id":"2408.02001","repositories_listed":1,"syntology":null},{"url":"/paper/2408-01284","slug":"2408-01284","title":"Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General Framework","date":"2024-08-02","arxiv_id":"2408.01284","repositories_listed":1,"syntology":null},{"url":"/paper/generative-diffusion-model-bootstraps-zero","slug":"generative-diffusion-model-bootstraps-zero","title":"Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations","date":"2024-07-29","arxiv_id":"2407.20072","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-foundation-models-for-zero-shot","slug":"leveraging-foundation-models-for-zero-shot","title":"Leveraging Foundation Models for Zero-Shot IoT Sensing","date":"2024-07-29","arxiv_id":"2407.19893","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-robustification-via-text-to-image","slug":"adversarial-robustification-via-text-to-image","title":"Adversarial Robustification via Text-to-Image Diffusion Models","date":"2024-07-26","arxiv_id":"2407.18658","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarial-robustification-via-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2407.18658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18658"}},"official":{"repos":["choidae1/robustify-t2i"],"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/is-larger-always-better-evaluating-and","slug":"is-larger-always-better-evaluating-and","title":"ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks","date":"2024-07-26","arxiv_id":"2407.18525","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/is-larger-always-better-evaluating-and#ran","syntology_url":"https://syntology.ai/paper/2407.18525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18525"}},"official":{"repos":["yhzhu99/ehr-llm-benchmark"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/i-can-listen-but-cannot-read-an-evaluation-of","slug":"i-can-listen-but-cannot-read-an-evaluation-of","title":"I can listen but cannot read: An evaluation of two-tower multimodal systems for instrument recognition","date":"2024-07-25","arxiv_id":"2407.18058","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-cluster-discrimination-for-visual","slug":"multi-label-cluster-discrimination-for-visual","title":"Multi-label Cluster Discrimination for Visual Representation Learning","date":"2024-07-24","arxiv_id":"2407.17331","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 7 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) · 4 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-label-cluster-discrimination-for-visual#ran","syntology_url":"https://syntology.ai/paper/2407.17331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.17331"}},"official":{"repos":["deepglint/unicom"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-semantic-decomposition-and-partial","slug":"visual-semantic-decomposition-and-partial","title":"Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot Learning","date":"2024-07-22","arxiv_id":"2407.15613","repositories_listed":1,"syntology":null},{"url":"/paper/esp-medsam-efficient-self-prompting-sam-for","slug":"esp-medsam-efficient-self-prompting-sam-for","title":"ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation","date":"2024-07-19","arxiv_id":"2407.14153","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-underwater-gesture-recognition","slug":"zero-shot-underwater-gesture-recognition","title":"Zero-Shot Underwater Gesture Recognition","date":"2024-07-19","arxiv_id":"2407.14103","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-simple-primitives-for-open","slug":"attention-based-simple-primitives-for-open","title":"Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning","date":"2024-07-18","arxiv_id":"2407.13715","repositories_listed":1,"syntology":null},{"url":"/paper/modalchorus-visual-probing-and-alignment-of","slug":"modalchorus-visual-probing-and-alignment-of","title":"ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map","date":"2024-07-17","arxiv_id":"2407.12315","repositories_listed":1,"syntology":null},{"url":"/paper/invagent-a-large-language-model-based-multi","slug":"invagent-a-large-language-model-based-multi","title":"InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains","date":"2024-07-16","arxiv_id":"2407.11384","repositories_listed":1,"syntology":null},{"url":"/paper/std-llm-understanding-both-spatial-and","slug":"std-llm-understanding-both-spatial-and","title":"STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM","date":"2024-07-12","arxiv_id":"2407.09096","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-text-based-quantitative-and","slug":"towards-a-text-based-quantitative-and","title":"Towards a text-based quantitative and explainable histopathology image analysis","date":"2024-07-10","arxiv_id":"2407.07360","repositories_listed":1,"syntology":null},{"url":"/paper/fairmedfm-fairness-benchmarking-for-medical","slug":"fairmedfm-fairness-benchmarking-for-medical","title":"FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models","date":"2024-07-01","arxiv_id":"2407.00983","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":0,"n_no_contract":2,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/fairmedfm-fairness-benchmarking-for-medical#ran","syntology_url":"https://syntology.ai/paper/2407.00983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.00983"}},"official":{"repos":["FairMedFM/FairMedFM"],"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/mitigate-the-gap-investigating-approaches-for","slug":"mitigate-the-gap-investigating-approaches-for","title":"Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP","date":"2024-06-25","arxiv_id":"2406.17639","repositories_listed":1,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":6,"n_honours":0,"n_violates":3,"n_no_contract":7,"n_pointer_only":22,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 3 violated, 7 with no contract checked; 6 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/mitigate-the-gap-investigating-approaches-for#ran","syntology_url":"https://syntology.ai/paper/2406.17639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17639"}},"official":{"repos":["sarahesl/alignclip"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluation-of-language-models-in-the-medical","slug":"evaluation-of-language-models-in-the-medical","title":"Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings","date":"2024-06-24","arxiv_id":"2406.16611","repositories_listed":1,"syntology":null},{"url":"/paper/clip-decoder-zeroshot-multilabel","slug":"clip-decoder-zeroshot-multilabel","title":"CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation","date":"2024-06-21","arxiv_id":"2406.14830","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clip-decoder-zeroshot-multilabel#ran","syntology_url":"https://syntology.ai/paper/2406.14830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14830"}},"official":null}},{"url":"/paper/contextual-interaction-via-primitive-based","slug":"contextual-interaction-via-primitive-based","title":"Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning","date":"2024-06-21","arxiv_id":"2406.14962","repositories_listed":1,"syntology":null},{"url":"/paper/a-data-driven-guided-decoding-mechanism-for","slug":"a-data-driven-guided-decoding-mechanism-for","title":"A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning","date":"2024-06-20","arxiv_id":"2406.14164","repositories_listed":1,"syntology":null},{"url":"/paper/part-aware-unified-representation-of-language-1","slug":"part-aware-unified-representation-of-language-1","title":"Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition","date":"2024-06-19","arxiv_id":"2406.13327","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":1,"n_no_contract":2,"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, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/part-aware-unified-representation-of-language-1#ran","syntology_url":"https://syntology.ai/paper/2406.13327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13327"}},"official":{"repos":["azzh1/purls"],"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/fusegen-plm-fusion-for-data-generation-based","slug":"fusegen-plm-fusion-for-data-generation-based","title":"FuseGen: PLM Fusion for Data-generation based Zero-shot Learning","date":"2024-06-18","arxiv_id":"2406.12527","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fusegen-plm-fusion-for-data-generation-based#ran","syntology_url":"https://syntology.ai/paper/2406.12527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12527"}},"official":{"repos":["LindaLydia/FuseGen"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/exploring-the-spectrum-of-visio-linguistic","slug":"exploring-the-spectrum-of-visio-linguistic","title":"Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition","date":"2024-06-13","arxiv_id":"2406.09388","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-visual-concepts-across-models","slug":"understanding-visual-concepts-across-models","title":"Understanding Visual Concepts Across Models","date":"2024-06-11","arxiv_id":"2406.07506","repositories_listed":1,"syntology":null},{"url":"/paper/bamo-at-semeval-2024-task-9-brainteaser-a","slug":"bamo-at-semeval-2024-task-9-brainteaser-a","title":"BAMO at SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense","date":"2024-06-07","arxiv_id":"2406.04947","repositories_listed":1,"syntology":null},{"url":"/paper/cplip-zero-shot-learning-for-histopathology","slug":"cplip-zero-shot-learning-for-histopathology","title":"CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment","date":"2024-06-07","arxiv_id":"2406.05205","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"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) · 4 unverified","sample_list":"/paper/cplip-zero-shot-learning-for-histopathology#ran","syntology_url":"https://syntology.ai/paper/2406.05205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05205"}},"official":{"repos":["iyyakuttiiyappan/CPLIP"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/countclip-re-teaching-clip-to-count-to-ten","slug":"countclip-re-teaching-clip-to-count-to-ten","title":"CountCLIP -- [Re] Teaching CLIP to Count to Ten","date":"2024-06-05","arxiv_id":"2406.03586","repositories_listed":1,"syntology":null},{"url":"/paper/description-boosting-for-zero-shot-entity-and","slug":"description-boosting-for-zero-shot-entity-and","title":"Description Boosting for Zero-Shot Entity and Relation Classification","date":"2024-06-04","arxiv_id":"2406.02245","repositories_listed":1,"syntology":null},{"url":"/paper/teii-think-explain-interact-and-iterate-with","slug":"teii-think-explain-interact-and-iterate-with","title":"TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection","date":"2024-05-27","arxiv_id":"2405.17129","repositories_listed":1,"syntology":null},{"url":"/paper/caps-adapter-caption-based-multimodal-adapter","slug":"caps-adapter-caption-based-multimodal-adapter","title":"CapS-Adapter: Caption-based MultiModal Adapter in Zero-Shot Classification","date":"2024-05-26","arxiv_id":"2405.16591","repositories_listed":1,"syntology":null},{"url":"/paper/darijabanking-a-new-resource-for-overcoming","slug":"darijabanking-a-new-resource-for-overcoming","title":"DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers","date":"2024-05-26","arxiv_id":"2405.16482","repositories_listed":1,"syntology":null},{"url":"/paper/chain-of-thought-prompting-for-demographic","slug":"chain-of-thought-prompting-for-demographic","title":"Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models","date":"2024-05-24","arxiv_id":"2405.15687","repositories_listed":1,"syntology":null},{"url":"/paper/clip-model-is-an-efficient-online-lifelong","slug":"clip-model-is-an-efficient-online-lifelong","title":"CLIP model is an Efficient Online Lifelong Learner","date":"2024-05-24","arxiv_id":"2405.15155","repositories_listed":1,"syntology":null},{"url":"/paper/what-do-you-see-enhancing-zero-shot-image","slug":"what-do-you-see-enhancing-zero-shot-image","title":"What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models","date":"2024-05-24","arxiv_id":"2405.15668","repositories_listed":1,"syntology":null},{"url":"/paper/harmony-a-joint-self-supervised-and-weakly","slug":"harmony-a-joint-self-supervised-and-weakly","title":"Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations","date":"2024-05-23","arxiv_id":"2405.14239","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-in-context-learning","slug":"implicit-in-context-learning","title":"Implicit In-context Learning","date":"2024-05-23","arxiv_id":"2405.14660","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/implicit-in-context-learning#ran","syntology_url":"https://syntology.ai/paper/2405.14660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14660"}},"official":{"repos":["lzvv123456/i2cl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/floor-plan-aided-indoor-localization-zero","slug":"floor-plan-aided-indoor-localization-zero","title":"Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype","date":"2024-05-22","arxiv_id":"2405.13339","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-and-modeling-social-intelligence-a","slug":"evaluating-and-modeling-social-intelligence-a","title":"Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities","date":"2024-05-20","arxiv_id":"2405.11841","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-model-scaling-using","slug":"differentiable-model-scaling-using","title":"Differentiable Model Scaling using Differentiable Topk","date":"2024-05-12","arxiv_id":"2405.07194","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/differentiable-model-scaling-using#ran","syntology_url":"https://syntology.ai/paper/2405.07194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07194"}},"official":{"repos":["LKJacky/Differentiable-Model-Scaling"],"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/medconceptsqa-open-source-medical-concepts-qa","slug":"medconceptsqa-open-source-medical-concepts-qa","title":"MedConceptsQA: Open Source Medical Concepts QA Benchmark","date":"2024-05-12","arxiv_id":"2405.07348","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":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/medconceptsqa-open-source-medical-concepts-qa#ran","syntology_url":"https://syntology.ai/paper/2405.07348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07348"}},"official":{"repos":["nadavlab/MedConceptsQA"],"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/pseudo-prompt-generating-in-pre-trained","slug":"pseudo-prompt-generating-in-pre-trained","title":"Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification","date":"2024-05-10","arxiv_id":"2405.06468","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-model-size-on-llm-post-hoc","slug":"the-effect-of-model-size-on-llm-post-hoc","title":"The Effect of Model Size on LLM Post-hoc Explainability via LIME","date":"2024-05-08","arxiv_id":"2405.05348","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/the-effect-of-model-size-on-llm-post-hoc#ran","syntology_url":"https://syntology.ai/paper/2405.05348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05348"}},"official":{"repos":["henningheyen/scalability-of-llm-posthoc-explanations"],"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/source-free-domain-adaptation-guided-by","slug":"source-free-domain-adaptation-guided-by","title":"Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training","date":"2024-05-05","arxiv_id":"2405.02954","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-chatgpt-for-diagnosing-autism","slug":"exploiting-chatgpt-for-diagnosing-autism","title":"Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features","date":"2024-05-03","arxiv_id":"2405.01799","repositories_listed":1,"syntology":null},{"url":"/paper/clipartt-light-weight-adaptation-of-clip-to","slug":"clipartt-light-weight-adaptation-of-clip-to","title":"CLIPArTT: Adaptation of CLIP to New Domains at Test Time","date":"2024-05-01","arxiv_id":"2405.00754","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":15,"phrase":"11 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; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/clipartt-light-weight-adaptation-of-clip-to#ran","syntology_url":"https://syntology.ai/paper/2405.00754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.00754"}},"official":{"repos":["dosowiechi/clipartt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/clip-mamba-clip-pretrained-mamba-models-with","slug":"clip-mamba-clip-pretrained-mamba-models-with","title":"CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation","date":"2024-04-30","arxiv_id":"2404.19394","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-caption-diversity-in-contrastive","slug":"modeling-caption-diversity-in-contrastive","title":"Modeling Caption Diversity in Contrastive Vision-Language Pretraining","date":"2024-04-30","arxiv_id":"2405.00740","repositories_listed":1,"syntology":{"n":22,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":22,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/modeling-caption-diversity-in-contrastive#ran","syntology_url":"https://syntology.ai/paper/2405.00740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.00740"}},"official":{"repos":["facebookresearch/llip"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/aapl-adding-attributes-to-prompt-learning-for","slug":"aapl-adding-attributes-to-prompt-learning-for","title":"AAPL: Adding Attributes to Prompt Learning for Vision-Language Models","date":"2024-04-25","arxiv_id":"2404.16804","repositories_listed":1,"syntology":null},{"url":"/paper/dual-expert-distillation-network-for","slug":"dual-expert-distillation-network-for","title":"Dual Expert Distillation Network for Generalized Zero-Shot Learning","date":"2024-04-25","arxiv_id":"2404.16348","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/dual-expert-distillation-network-for#ran","syntology_url":"https://syntology.ai/paper/2404.16348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16348"}},"official":{"repos":["zjrao/dedn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/opendlign-enhancing-open-world-3d-learning","slug":"opendlign-enhancing-open-world-3d-learning","title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","date":"2024-04-25","arxiv_id":"2404.16538","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opendlign-enhancing-open-world-3d-learning#ran","syntology_url":"https://syntology.ai/paper/2404.16538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16538"}},"official":{"repos":["Yebulabula/OpenDlign"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/crest-cross-modal-resonance-through","slug":"crest-cross-modal-resonance-through","title":"CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning","date":"2024-04-15","arxiv_id":"2404.09640","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-enhanced-visual-language","slug":"knowledge-enhanced-visual-language","title":"Knowledge-enhanced Visual-Language Pretraining for Computational Pathology","date":"2024-04-15","arxiv_id":"2404.09942","repositories_listed":1,"syntology":null},{"url":"/paper/the-devil-is-in-the-few-shots-iterative","slug":"the-devil-is-in-the-few-shots-iterative","title":"The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning","date":"2024-04-15","arxiv_id":"2404.09778","repositories_listed":1,"syntology":null},{"url":"/paper/customising-general-large-language-models-for-1","slug":"customising-general-large-language-models-for-1","title":"Customising General Large Language Models for Specialised Emotion Recognition Tasks","date":"2024-04-14","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"8587f58e03577f7f6d244e1f95f9fa30ce7397852eb95c346940ab7fbef637b5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}