{"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/decoder/papers/22","list_of":"/task/decoder","task":"Decoder","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":22,"pages_in_order":104,"rows_per_page":100,"rows":[2101,2200],"of":10368,"counts":{"archive_papers_tagged":10368,"with_a_code_link":4358,"where_syntology_ran_a_sample":1061,"not_listed_spam_title":0,"listed":10368,"listed_where_code_ran":1061,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":152,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":152,"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/decoder","prev":"/task/decoder/papers/21","next":"/task/decoder/papers/23","papers":[{"url":"/paper/unichart-a-universal-vision-language","slug":"unichart-a-universal-vision-language","title":"UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning","date":"2023-05-24","arxiv_id":"2305.14761","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/unichart-a-universal-vision-language#ran","syntology_url":"https://syntology.ai/paper/2305.14761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14761"}},"official":{"repos":["vis-nlp/unichart"],"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/better-low-resource-entity-recognition","slug":"better-low-resource-entity-recognition","title":"Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction","date":"2023-05-23","arxiv_id":"2305.13582","repositories_listed":1,"syntology":null},{"url":"/paper/clip4str-a-simple-baseline-for-scene-text-1","slug":"clip4str-a-simple-baseline-for-scene-text-1","title":"CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model","date":"2023-05-23","arxiv_id":"2305.14014","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/clip4str-a-simple-baseline-for-scene-text-1#ran","syntology_url":"https://syntology.ai/paper/2305.14014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14014"}},"official":null}},{"url":"/paper/exploring-large-language-models-for-classical","slug":"exploring-large-language-models-for-classical","title":"Exploring Large Language Models for Classical Philology","date":"2023-05-23","arxiv_id":"2305.13698","repositories_listed":1,"syntology":null},{"url":"/paper/mp-senet-a-speech-enhancement-model-with","slug":"mp-senet-a-speech-enhancement-model-with","title":"MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra","date":"2023-05-23","arxiv_id":"2305.13686","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/mp-senet-a-speech-enhancement-model-with#ran","syntology_url":"https://syntology.ai/paper/2305.13686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13686"}},"official":{"repos":["yxlu-0102/MP-SENet"],"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/neural-image-re-exposure","slug":"neural-image-re-exposure","title":"Neural Image Re-Exposure","date":"2023-05-23","arxiv_id":"2305.13593","repositories_listed":1,"syntology":null},{"url":"/paper/on-robustness-of-finetuned-transformer-based","slug":"on-robustness-of-finetuned-transformer-based","title":"On Robustness of Finetuned Transformer-based NLP Models","date":"2023-05-23","arxiv_id":"2305.14453","repositories_listed":1,"syntology":null},{"url":"/paper/bidirectional-transformer-reranker-for","slug":"bidirectional-transformer-reranker-for","title":"Bidirectional Transformer Reranker for Grammatical Error Correction","date":"2023-05-22","arxiv_id":"2305.13000","repositories_listed":1,"syntology":null},{"url":"/paper/fit-far-reaching-interleaved-transformers","slug":"fit-far-reaching-interleaved-transformers","title":"FIT: Far-reaching Interleaved Transformers","date":"2023-05-22","arxiv_id":"2305.12689","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/fit-far-reaching-interleaved-transformers#ran","syntology_url":"https://syntology.ai/paper/2305.12689","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12689"}},"official":{"repos":["google-research/pix2seq"],"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/maclasa-multi-aspect-controllable-text","slug":"maclasa-multi-aspect-controllable-text","title":"MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space","date":"2023-05-22","arxiv_id":"2305.12785","repositories_listed":1,"syntology":null},{"url":"/paper/u-tilise-a-sequence-to-sequence-model-for","slug":"u-tilise-a-sequence-to-sequence-model-for","title":"U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series","date":"2023-05-22","arxiv_id":"2305.13277","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/u-tilise-a-sequence-to-sequence-model-for#ran","syntology_url":"https://syntology.ai/paper/2305.13277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13277"}},"official":{"repos":["prs-eth/u-tilise"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/videollm-modeling-video-sequence-with-large","slug":"videollm-modeling-video-sequence-with-large","title":"VideoLLM: Modeling Video Sequence with Large Language Models","date":"2023-05-22","arxiv_id":"2305.13292","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-for-bidirectional-decoding-case","slug":"a-framework-for-bidirectional-decoding-case","title":"A Framework for Bidirectional Decoding: Case Study in Morphological Inflection","date":"2023-05-21","arxiv_id":"2305.12580","repositories_listed":1,"syntology":null},{"url":"/paper/do-we-need-an-encoder-decoder-to-model","slug":"do-we-need-an-encoder-decoder-to-model","title":"Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?","date":"2023-05-20","arxiv_id":"2305.12185","repositories_listed":1,"syntology":null},{"url":"/paper/jetseg-efficient-real-time-semantic","slug":"jetseg-efficient-real-time-semantic","title":"JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems","date":"2023-05-19","arxiv_id":"2305.11419","repositories_listed":1,"syntology":null},{"url":"/paper/pointgpt-auto-regressively-generative-pre-1","slug":"pointgpt-auto-regressively-generative-pre-1","title":"PointGPT: Auto-regressively Generative Pre-training from Point Clouds","date":"2023-05-19","arxiv_id":"2305.11487","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pointgpt-auto-regressively-generative-pre-1#ran","syntology_url":"https://syntology.ai/paper/2305.11487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11487"}},"official":{"repos":["CGuangyan-BIT/PointGPT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-parameter-efficient-learning-approach-to","slug":"a-parameter-efficient-learning-approach-to","title":"A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model","date":"2023-05-18","arxiv_id":"2305.11244","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-efficient-learning-for-text-to","slug":"parameter-efficient-learning-for-text-to","title":"Parameter-Efficient Learning for Text-to-Speech Accent Adaptation","date":"2023-05-18","arxiv_id":"2305.11320","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-supervised-target-extraction-method","slug":"scribble-supervised-target-extraction-method","title":"Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images","date":"2023-05-18","arxiv_id":"2305.10661","repositories_listed":1,"syntology":null},{"url":"/paper/tram-a-token-level-retrieval-augmented","slug":"tram-a-token-level-retrieval-augmented","title":"Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization","date":"2023-05-18","arxiv_id":"2305.11074","repositories_listed":1,"syntology":null},{"url":"/paper/multiplanenerf-neural-radiance-field-with-non","slug":"multiplanenerf-neural-radiance-field-with-non","title":"MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation","date":"2023-05-17","arxiv_id":"2305.10579","repositories_listed":1,"syntology":null},{"url":"/paper/object-segmentation-by-mining-cross-modal","slug":"object-segmentation-by-mining-cross-modal","title":"Object Segmentation by Mining Cross-Modal Semantics","date":"2023-05-17","arxiv_id":"2305.10469","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-global-context-cross-consistency","slug":"multi-level-global-context-cross-consistency","title":"Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model","date":"2023-05-16","arxiv_id":"2305.09447","repositories_listed":1,"syntology":null},{"url":"/paper/nighthazeformer-single-nighttime-haze-removal","slug":"nighthazeformer-single-nighttime-haze-removal","title":"NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer","date":"2023-05-16","arxiv_id":"2305.09533","repositories_listed":1,"syntology":null},{"url":"/paper/a-hierarchical-encoding-decoding-scheme-for","slug":"a-hierarchical-encoding-decoding-scheme-for","title":"A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization","date":"2023-05-15","arxiv_id":"2305.08503","repositories_listed":1,"syntology":null},{"url":"/paper/geomae-masked-geometric-target-prediction-for","slug":"geomae-masked-geometric-target-prediction-for","title":"GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training","date":"2023-05-15","arxiv_id":"2305.08808","repositories_listed":1,"syntology":null},{"url":"/paper/combo-of-thinking-and-observing-for-outside","slug":"combo-of-thinking-and-observing-for-outside","title":"Combo of Thinking and Observing for Outside-Knowledge VQA","date":"2023-05-10","arxiv_id":"2305.06407","repositories_listed":1,"syntology":null},{"url":"/paper/sepmark-deep-separable-watermarking-for","slug":"sepmark-deep-separable-watermarking-for","title":"SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection","date":"2023-05-10","arxiv_id":"2305.06321","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/sepmark-deep-separable-watermarking-for#ran","syntology_url":"https://syntology.ai/paper/2305.06321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06321"}},"official":{"repos":["sh1newu/sepmark"],"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/shs-net-learning-signed-hyper-surfaces-for","slug":"shs-net-learning-signed-hyper-surfaces-for","title":"Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation","date":"2023-05-10","arxiv_id":"2305.05873","repositories_listed":1,"syntology":null},{"url":"/paper/think-twice-before-driving-towards-scalable","slug":"think-twice-before-driving-towards-scalable","title":"Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving","date":"2023-05-10","arxiv_id":"2305.06242","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":0,"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/think-twice-before-driving-towards-scalable#ran","syntology_url":"https://syntology.ai/paper/2305.06242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06242"}},"official":{"repos":["opendrivelab/thinktwice"],"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/3dinvnet-a-deep-learning-based-3d-ground","slug":"3dinvnet-a-deep-learning-based-3d-ground","title":"3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion","date":"2023-05-09","arxiv_id":"2305.05425","repositories_listed":1,"syntology":null},{"url":"/paper/a-mountain-shaped-single-stage-network-for","slug":"a-mountain-shaped-single-stage-network-for","title":"A Mountain-Shaped Single-Stage Network for Accurate Image Restoration","date":"2023-05-09","arxiv_id":"2305.05146","repositories_listed":1,"syntology":null},{"url":"/paper/an-exploration-of-encoder-decoder-approaches","slug":"an-exploration-of-encoder-decoder-approaches","title":"An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text","date":"2023-05-09","arxiv_id":"2305.05627","repositories_listed":1,"syntology":null},{"url":"/paper/can-point-cloud-networks-learn-statistical","slug":"can-point-cloud-networks-learn-statistical","title":"Can point cloud networks learn statistical shape models of anatomies?","date":"2023-05-09","arxiv_id":"2305.05610","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/can-point-cloud-networks-learn-statistical#ran","syntology_url":"https://syntology.ai/paper/2305.05610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05610"}},"official":{"repos":["jadie1/pointcompletionssm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/e2timt-efficient-and-effective-modal-adapter","slug":"e2timt-efficient-and-effective-modal-adapter","title":"E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation","date":"2023-05-09","arxiv_id":"2305.05166","repositories_listed":1,"syntology":null},{"url":"/paper/multi-teacher-knowledge-distillation-for-text","slug":"multi-teacher-knowledge-distillation-for-text","title":"Multi-Teacher Knowledge Distillation For Text Image Machine Translation","date":"2023-05-09","arxiv_id":"2305.05226","repositories_listed":1,"syntology":null},{"url":"/paper/a-unifying-framework-of-attention-based","slug":"a-unifying-framework-of-attention-based","title":"A Unifying Framework of Attention-based Neural Load Forecasting","date":"2023-05-08","arxiv_id":"2305.05082","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-parallel-rcnn-with-novel-feature","slug":"explainable-parallel-rcnn-with-novel-feature","title":"Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting","date":"2023-05-08","arxiv_id":"2305.04876","repositories_listed":1,"syntology":null},{"url":"/paper/joint-moment-retrieval-and-highlight","slug":"joint-moment-retrieval-and-highlight","title":"Joint Moment Retrieval and Highlight Detection Via Natural Language Queries","date":"2023-05-08","arxiv_id":"2305.04961","repositories_listed":1,"syntology":null},{"url":"/paper/swindocsegmenter-an-end-to-end-unified-domain","slug":"swindocsegmenter-an-end-to-end-unified-domain","title":"SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation","date":"2023-05-08","arxiv_id":"2305.04609","repositories_listed":1,"syntology":null},{"url":"/paper/toward-adversarial-training-on-contextualized","slug":"toward-adversarial-training-on-contextualized","title":"Toward Adversarial Training on Contextualized Language Representation","date":"2023-05-08","arxiv_id":"2305.04557","repositories_listed":1,"syntology":null},{"url":"/paper/adaptiveclick-clicks-aware-transformer-with","slug":"adaptiveclick-clicks-aware-transformer-with","title":"AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation","date":"2023-05-07","arxiv_id":"2305.04276","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptiveclick-clicks-aware-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2305.04276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04276"}},"official":{"repos":["lab206/adaptiveclick"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/norbench-a-benchmark-for-norwegian-language","slug":"norbench-a-benchmark-for-norwegian-language","title":"NorBench -- A Benchmark for Norwegian Language Models","date":"2023-05-06","arxiv_id":"2305.03880","repositories_listed":1,"syntology":null},{"url":"/paper/towards-capturing-the-temporal-dynamics-for","slug":"towards-capturing-the-temporal-dynamics-for","title":"Towards Capturing the Temporal Dynamics for Trajectory Prediction: a Coarse-to-Fine Approach","date":"2023-05-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dspdet3d-dynamic-spatial-pruning-for-3d-small","slug":"dspdet3d-dynamic-spatial-pruning-for-3d-small","title":"3D Small Object Detection with Dynamic Spatial Pruning","date":"2023-05-05","arxiv_id":"2305.03716","repositories_listed":1,"syntology":null},{"url":"/paper/lssed-a-robust-segmentation-network-for","slug":"lssed-a-robust-segmentation-network-for","title":"LSSED: A Robust Segmentation Network for Inflamed Appendix from CT Images","date":"2023-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-graph-representation-learning-for","slug":"multi-view-graph-representation-learning-for","title":"Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question","date":"2023-05-05","arxiv_id":"2305.03458","repositories_listed":1,"syntology":null},{"url":"/paper/transesc-smoothing-emotional-support","slug":"transesc-smoothing-emotional-support","title":"TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition","date":"2023-05-05","arxiv_id":"2305.03296","repositories_listed":1,"syntology":null},{"url":"/paper/fusegnet-a-deep-convolutional-neural-network","slug":"fusegnet-a-deep-convolutional-neural-network","title":"FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation","date":"2023-05-04","arxiv_id":"2305.02961","repositories_listed":1,"syntology":null},{"url":"/paper/gcrdn-global-context-driven-residual-dense","slug":"gcrdn-global-context-driven-residual-dense","title":"GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution","date":"2023-05-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentence-embedding-leaks-more-information","slug":"sentence-embedding-leaks-more-information","title":"Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence","date":"2023-05-04","arxiv_id":"2305.03010","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 7 unverified","sample_list":"/paper/sentence-embedding-leaks-more-information#ran","syntology_url":"https://syntology.ai/paper/2305.03010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03010"}},"official":{"repos":["hkust-knowcomp/geia"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/alleviating-exposure-bias-via-multi-level","slug":"alleviating-exposure-bias-via-multi-level","title":"Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization","date":"2023-05-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transforming-visual-scene-graphs-to-image","slug":"transforming-visual-scene-graphs-to-image","title":"Transforming Visual Scene Graphs to Image Captions","date":"2023-05-03","arxiv_id":"2305.02177","repositories_listed":1,"syntology":null},{"url":"/paper/flightbert-a-non-autoregressive-multi-horizon","slug":"flightbert-a-non-autoregressive-multi-horizon","title":"A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation","date":"2023-05-02","arxiv_id":"2305.01658","repositories_listed":1,"syntology":null},{"url":"/paper/inconseg-residual-guided-fusion-with","slug":"inconseg-residual-guided-fusion-with","title":"InconSeg: Residual-Guided Fusion With Inconsistent Multi-Modal Data for Negative and Positive Road Obstacles Segmentation","date":"2023-05-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mafid-moving-average-equipped-fusion-in","slug":"mafid-moving-average-equipped-fusion-in","title":"MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data","date":"2023-05-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/oil-spill-segmentation-using-deep-encoder","slug":"oil-spill-segmentation-using-deep-encoder","title":"Oil Spill Segmentation using Deep Encoder-Decoder models","date":"2023-05-02","arxiv_id":"2305.01386","repositories_listed":1,"syntology":null},{"url":"/paper/otiea-ontology-enhanced-triple-intrinsic","slug":"otiea-ontology-enhanced-triple-intrinsic","title":"OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment","date":"2023-05-02","arxiv_id":"2305.01561","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-boundary-detection-in-deep","slug":"rethinking-boundary-detection-in-deep","title":"Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation","date":"2023-05-01","arxiv_id":"2305.00678","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-voxelization-of-dynamic-scenes","slug":"object-centric-voxelization-of-dynamic-scenes","title":"DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization","date":"2023-04-30","arxiv_id":"2305.00393","repositories_listed":1,"syntology":null},{"url":"/paper/mh-detr-video-moment-and-highlight-detection","slug":"mh-detr-video-moment-and-highlight-detection","title":"MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer","date":"2023-04-29","arxiv_id":"2305.00355","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-enhanced-model-for-live-video","slug":"knowledge-enhanced-model-for-live-video","title":"Knowledge Enhanced Model for Live Video Comment Generation","date":"2023-04-28","arxiv_id":"2304.14657","repositories_listed":1,"syntology":null},{"url":"/paper/learning-locally-editable-virtual-humans","slug":"learning-locally-editable-virtual-humans","title":"Learning Locally Editable Virtual Humans","date":"2023-04-28","arxiv_id":"2305.00121","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":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-locally-editable-virtual-humans#ran","syntology_url":"https://syntology.ai/paper/2305.00121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00121"}},"official":{"repos":["custom-humans/editable-humans"],"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/lamini-lm-a-diverse-herd-of-distilled-models","slug":"lamini-lm-a-diverse-herd-of-distilled-models","title":"LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions","date":"2023-04-27","arxiv_id":"2304.14402","repositories_listed":1,"syntology":null},{"url":"/paper/seqtrack-sequence-to-sequence-learning-for","slug":"seqtrack-sequence-to-sequence-learning-for","title":"Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking","date":"2023-04-27","arxiv_id":"2304.14394","repositories_listed":1,"syntology":null},{"url":"/paper/ucf-uncovering-common-features-for","slug":"ucf-uncovering-common-features-for","title":"UCF: Uncovering Common Features for Generalizable Deepfake Detection","date":"2023-04-27","arxiv_id":"2304.13949","repositories_listed":1,"syntology":null},{"url":"/paper/a-symmetric-dual-encoding-dense-retrieval","slug":"a-symmetric-dual-encoding-dense-retrieval","title":"A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering","date":"2023-04-26","arxiv_id":"2304.13649","repositories_listed":1,"syntology":null},{"url":"/paper/customized-segment-anything-model-for-medical","slug":"customized-segment-anything-model-for-medical","title":"Customized Segment Anything Model for Medical Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13785","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/customized-segment-anything-model-for-medical#ran","syntology_url":"https://syntology.ai/paper/2304.13785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13785"}},"official":{"repos":["hitachinsk/samed"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/from-association-to-generation-text-only","slug":"from-association-to-generation-text-only","title":"From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping","date":"2023-04-26","arxiv_id":"2304.13273","repositories_listed":1,"syntology":null},{"url":"/paper/sdsc-unet-dual-skip-connection-vit-based-u","slug":"sdsc-unet-dual-skip-connection-vit-based-u","title":"SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction","date":"2023-04-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/walking-your-lidog-a-journey-through-multiple","slug":"walking-your-lidog-a-journey-through-multiple","title":"Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation","date":"2023-04-23","arxiv_id":"2304.11705","repositories_listed":1,"syntology":null},{"url":"/paper/dilated-unet-a-fast-and-accurate-medical","slug":"dilated-unet-a-fast-and-accurate-medical","title":"Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture","date":"2023-04-22","arxiv_id":"2304.11450","repositories_listed":1,"syntology":null},{"url":"/paper/lidar2map-in-defense-of-lidar-based-semantic","slug":"lidar2map-in-defense-of-lidar-based-semantic","title":"LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation","date":"2023-04-22","arxiv_id":"2304.11379","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":2,"n_no_contract":10,"n_pointer_only":3,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 2 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/lidar2map-in-defense-of-lidar-based-semantic#ran","syntology_url":"https://syntology.ai/paper/2304.11379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11379"}},"official":{"repos":["songw-zju/lidar2map"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/nelora-bench-a-benchmark-for-neural-enhanced","slug":"nelora-bench-a-benchmark-for-neural-enhanced","title":"NELoRa-Bench: A Benchmark for Neural-enhanced LoRa Demodulation","date":"2023-04-20","arxiv_id":"2305.01573","repositories_listed":1,"syntology":null},{"url":"/paper/learning-situation-hyper-graphs-for-video","slug":"learning-situation-hyper-graphs-for-video","title":"Learning Situation Hyper-Graphs for Video Question Answering","date":"2023-04-18","arxiv_id":"2304.08682","repositories_listed":1,"syntology":{"n":15,"n_ran":8,"n_constructed":5,"n_ran_checked":8,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"8 ran (of which 5 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) · 7 unverified","sample_list":"/paper/learning-situation-hyper-graphs-for-video#ran","syntology_url":"https://syntology.ai/paper/2304.08682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08682"}},"official":{"repos":["aurooj/shg-vqa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":8,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/an-empirical-study-of-multitask-learning-to","slug":"an-empirical-study-of-multitask-learning-to","title":"An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems","date":"2023-04-17","arxiv_id":"2304.08115","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-compress-prompts-with-gist-tokens","slug":"learning-to-compress-prompts-with-gist-tokens","title":"Learning to Compress Prompts with Gist Tokens","date":"2023-04-17","arxiv_id":"2304.08467","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/learning-to-compress-prompts-with-gist-tokens#ran","syntology_url":"https://syntology.ai/paper/2304.08467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08467"}},"official":{"repos":["jayelm/gisting"],"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/prak-an-automatic-phonetic-alignment-tool-for","slug":"prak-an-automatic-phonetic-alignment-tool-for","title":"Prak: An automatic phonetic alignment tool for Czech","date":"2023-04-17","arxiv_id":"2304.08431","repositories_listed":1,"syntology":null},{"url":"/paper/typos-aware-bottlenecked-pre-training-for","slug":"typos-aware-bottlenecked-pre-training-for","title":"Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval","date":"2023-04-17","arxiv_id":"2304.08138","repositories_listed":1,"syntology":null},{"url":"/paper/valor-vision-audio-language-omni-perception","slug":"valor-vision-audio-language-omni-perception","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","date":"2023-04-17","arxiv_id":"2304.08345","repositories_listed":1,"syntology":null},{"url":"/paper/3d-feature-prediction-for-masked-autoencoder","slug":"3d-feature-prediction-for-masked-autoencoder","title":"3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining","date":"2023-04-14","arxiv_id":"2304.06911","repositories_listed":1,"syntology":null},{"url":"/paper/bitstream-corrupted-jpeg-images-are","slug":"bitstream-corrupted-jpeg-images-are","title":"Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration","date":"2023-04-14","arxiv_id":"2304.06976","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/bitstream-corrupted-jpeg-images-are#ran","syntology_url":"https://syntology.ai/paper/2304.06976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06976"}},"official":{"repos":["wenyang001/two-acir"],"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/sub-meter-resolution-canopy-height-maps-using","slug":"sub-meter-resolution-canopy-height-maps-using","title":"Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar","date":"2023-04-14","arxiv_id":"2304.07213","repositories_listed":1,"syntology":null},{"url":"/paper/shall-we-pretrain-autoregressive-language","slug":"shall-we-pretrain-autoregressive-language","title":"Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study","date":"2023-04-13","arxiv_id":"2304.06762","repositories_listed":1,"syntology":null},{"url":"/paper/single-stage-diffusion-nerf-a-unified","slug":"single-stage-diffusion-nerf-a-unified","title":"Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction","date":"2023-04-13","arxiv_id":"2304.06714","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-human-matting-for-dynamic-videos","slug":"adaptive-human-matting-for-dynamic-videos","title":"Adaptive Human Matting for Dynamic Videos","date":"2023-04-12","arxiv_id":"2304.06018","repositories_listed":1,"syntology":null},{"url":"/paper/data-efficient-image-quality-assessment-with","slug":"data-efficient-image-quality-assessment-with","title":"Data-Efficient Image Quality Assessment with Attention-Panel Decoder","date":"2023-04-11","arxiv_id":"2304.04952","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"2 ran (of which 1 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) · 3 unverified","sample_list":"/paper/data-efficient-image-quality-assessment-with#ran","syntology_url":"https://syntology.ai/paper/2304.04952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04952"}},"official":{"repos":["narthchin/deiqt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/occformer-dual-path-transformer-for-vision","slug":"occformer-dual-path-transformer-for-vision","title":"OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction","date":"2023-04-11","arxiv_id":"2304.05316","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/occformer-dual-path-transformer-for-vision#ran","syntology_url":"https://syntology.ai/paper/2304.05316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05316"}},"official":{"repos":["zhangyp15/occformer"],"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/relational-context-learning-for-human-object","slug":"relational-context-learning-for-human-object","title":"Relational Context Learning for Human-Object Interaction Detection","date":"2023-04-11","arxiv_id":"2304.04997","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":4,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":12,"phrase":"10 ran (of which 4 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) · 2 unverified","sample_list":"/paper/relational-context-learning-for-human-object#ran","syntology_url":"https://syntology.ai/paper/2304.04997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04997"}},"official":{"repos":["OreoChocolate/MUREN"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stageinteractor-query-based-object-detector","slug":"stageinteractor-query-based-object-detector","title":"StageInteractor: Query-based Object Detector with Cross-stage Interaction","date":"2023-04-11","arxiv_id":"2304.04978","repositories_listed":1,"syntology":null},{"url":"/paper/co-attention-propagation-network-for-zero","slug":"co-attention-propagation-network-for-zero","title":"Co-attention Propagation Network for Zero-Shot Video Object Segmentation","date":"2023-04-08","arxiv_id":"2304.03910","repositories_listed":1,"syntology":null},{"url":"/paper/adjustable-privacy-using-autoencoder-based","slug":"adjustable-privacy-using-autoencoder-based","title":"Adjustable Privacy using Autoencoder-based Learning Structure","date":"2023-04-07","arxiv_id":"2304.03538","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-deep-learning-for-variable-type","slug":"revisiting-deep-learning-for-variable-type","title":"Revisiting Deep Learning for Variable Type Recovery","date":"2023-04-07","arxiv_id":"2304.03854","repositories_listed":1,"syntology":null},{"url":"/paper/uniseg-a-prompt-driven-universal-segmentation","slug":"uniseg-a-prompt-driven-universal-segmentation","title":"UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner","date":"2023-04-07","arxiv_id":"2304.03493","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uniseg-a-prompt-driven-universal-segmentation#ran","syntology_url":"https://syntology.ai/paper/2304.03493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03493"}},"official":{"repos":["yeerwen/uniseg"],"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/boundary-denoising-for-video-activity","slug":"boundary-denoising-for-video-activity","title":"Boundary-Denoising for Video Activity Localization","date":"2023-04-06","arxiv_id":"2304.02934","repositories_listed":1,"syntology":null},{"url":"/paper/hnerv-a-hybrid-neural-representation-for","slug":"hnerv-a-hybrid-neural-representation-for","title":"HNeRV: A Hybrid Neural Representation for Videos","date":"2023-04-05","arxiv_id":"2304.02633","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"8 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hnerv-a-hybrid-neural-representation-for#ran","syntology_url":"https://syntology.ai/paper/2304.02633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02633"}},"official":{"repos":["haochen-rye/hnerv"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-vision-language-models-for","slug":"exploring-vision-language-models-for","title":"Exploring Vision-Language Models for Imbalanced Learning","date":"2023-04-04","arxiv_id":"2304.01457","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/exploring-vision-language-models-for#ran","syntology_url":"https://syntology.ai/paper/2304.01457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01457"}},"official":{"repos":["imbalance-vlm/imbalance-vlm"],"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/hyperthumbnail-real-time-6k-image-rescaling","slug":"hyperthumbnail-real-time-6k-image-rescaling","title":"Real-time 6K Image Rescaling with Rate-distortion Optimization","date":"2023-04-03","arxiv_id":"2304.01064","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/hyperthumbnail-real-time-6k-image-rescaling#ran","syntology_url":"https://syntology.ai/paper/2304.01064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01064"}},"official":{"repos":["abnervictor/hyperthumbnail"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-context-aggregation-in-natural","slug":"rethinking-context-aggregation-in-natural","title":"Revisiting Context Aggregation for Image Matting","date":"2023-04-03","arxiv_id":"2304.01171","repositories_listed":1,"syntology":null},{"url":"/paper/weaktr-exploring-plain-vision-transformer-for","slug":"weaktr-exploring-plain-vision-transformer-for","title":"WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation","date":"2023-04-03","arxiv_id":"2304.01184","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/weaktr-exploring-plain-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2304.01184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01184"}},"official":{"repos":["hustvl/weaktr"],"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"]}}}],"record_sha256":"423c5465b1a98dd7f1e400e0f3e2d41a86d10cd0febb2cc79e8dab02263df498","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}