{"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/32","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":32,"pages_in_order":104,"rows_per_page":100,"rows":[3101,3200],"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/31","next":"/task/decoder/papers/33","papers":[{"url":"/paper/automatic-foot-ulcer-segmentation-using-an","slug":"automatic-foot-ulcer-segmentation-using-an","title":"Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks","date":"2021-09-03","arxiv_id":"2109.01408","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-representation-learning-for","slug":"contrastive-representation-learning-for","title":"Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation","date":"2021-09-03","arxiv_id":"2109.01484","repositories_listed":1,"syntology":null},{"url":"/paper/fbsnet-a-fast-bilateral-symmetrical-network","slug":"fbsnet-a-fast-bilateral-symmetrical-network","title":"FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation","date":"2021-09-02","arxiv_id":"2109.00699","repositories_listed":1,"syntology":null},{"url":"/paper/deep-dual-support-vector-data-description-for","slug":"deep-dual-support-vector-data-description-for","title":"Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks","date":"2021-09-01","arxiv_id":"2109.00138","repositories_listed":1,"syntology":null},{"url":"/paper/on-generating-fact-infused-question","slug":"on-generating-fact-infused-question","title":"On Generating Fact-Infused Question Variations","date":"2021-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/optagan-entropy-based-finetuning-on-text-vae","slug":"optagan-entropy-based-finetuning-on-text-vae","title":"OptAGAN: Entropy-based finetuning on text VAE-GAN","date":"2021-09-01","arxiv_id":"2109.00239","repositories_listed":1,"syntology":null},{"url":"/paper/point-cloud-pre-training-by-mixing-and","slug":"point-cloud-pre-training-by-mixing-and","title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","date":"2021-09-01","arxiv_id":"2109.00452","repositories_listed":1,"syntology":null},{"url":"/paper/sentence-bottleneck-autoencoders-from","slug":"sentence-bottleneck-autoencoders-from","title":"Sentence Bottleneck Autoencoders from Transformer Language Models","date":"2021-08-31","arxiv_id":"2109.00055","repositories_listed":1,"syntology":null},{"url":"/paper/ko-codes-inventing-nonlinear-encoding-and","slug":"ko-codes-inventing-nonlinear-encoding-and","title":"KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning","date":"2021-08-29","arxiv_id":"2108.12920","repositories_listed":1,"syntology":null},{"url":"/paper/smoothing-dialogue-states-for-open","slug":"smoothing-dialogue-states-for-open","title":"Smoothing Dialogue States for Open Conversational Machine Reading","date":"2021-08-28","arxiv_id":"2108.12599","repositories_listed":1,"syntology":null},{"url":"/paper/alleviating-exposure-bias-via-contrastive","slug":"alleviating-exposure-bias-via-contrastive","title":"Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization","date":"2021-08-26","arxiv_id":"2108.11846","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":2,"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/alleviating-exposure-bias-via-contrastive#ran","syntology_url":"https://syntology.ai/paper/2108.11846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.11846"}},"official":{"repos":["shichaosun/conabssum"],"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/estimation-of-road-boundary-for-intelligent","slug":"estimation-of-road-boundary-for-intelligent","title":"Estimation of Road Boundary for Intelligent Vehicles Based on DeepLabV3+ Architecture","date":"2021-08-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/real-time-monocular-human-depth-estimation","slug":"real-time-monocular-human-depth-estimation","title":"Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems","date":"2021-08-24","arxiv_id":"2108.10506","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-depth-completion-with-calibrated","slug":"unsupervised-depth-completion-with-calibrated","title":"Unsupervised Depth Completion with Calibrated Backprojection Layers","date":"2021-08-24","arxiv_id":"2108.10531","repositories_listed":1,"syntology":null},{"url":"/paper/canet-a-context-aware-network-for-shadow","slug":"canet-a-context-aware-network-for-shadow","title":"CANet: A Context-Aware Network for Shadow Removal","date":"2021-08-23","arxiv_id":"2108.09894","repositories_listed":1,"syntology":null},{"url":"/paper/arapreg-an-as-rigid-as-possible","slug":"arapreg-an-as-rigid-as-possible","title":"ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators","date":"2021-08-21","arxiv_id":"2108.09432","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-edge-based-u-shape-network-for","slug":"multi-scale-edge-based-u-shape-network-for","title":"Multi-scale Edge-based U-shape Network for Salient Object Detection","date":"2021-08-21","arxiv_id":"2108.09408","repositories_listed":1,"syntology":null},{"url":"/paper/frozen-pretrained-transformers-for-neural","slug":"frozen-pretrained-transformers-for-neural","title":"Frozen Pretrained Transformers for Neural Sign Language Translation","date":"2021-08-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/trans4trans-efficient-transformer-for-1","slug":"trans4trans-efficient-transformer-for-1","title":"Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance","date":"2021-08-20","arxiv_id":"2108.09174","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-remedies-for-outlier-detection-with","slug":"efficient-remedies-for-outlier-detection-with","title":"Robust outlier detection by de-biasing VAE likelihoods","date":"2021-08-19","arxiv_id":"2108.08760","repositories_listed":1,"syntology":null},{"url":"/paper/pointr-diverse-point-cloud-completion-with","slug":"pointr-diverse-point-cloud-completion-with","title":"PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers","date":"2021-08-19","arxiv_id":"2108.08839","repositories_listed":1,"syntology":null},{"url":"/paper/video-relation-detection-via-tracklet-based","slug":"video-relation-detection-via-tracklet-based","title":"Video Relation Detection via Tracklet based Visual Transformer","date":"2021-08-19","arxiv_id":"2108.08669","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-perturbations-with-normalizing-flows","slug":"semantic-perturbations-with-normalizing-flows","title":"Semantic Perturbations with Normalizing Flows for Improved Generalization","date":"2021-08-18","arxiv_id":"2108.07958","repositories_listed":1,"syntology":null},{"url":"/paper/towards-deep-and-efficient-a-deep-siamese","slug":"towards-deep-and-efficient-a-deep-siamese","title":"Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images","date":"2021-08-18","arxiv_id":"2108.08157","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-salient-object-detection-with","slug":"boosting-salient-object-detection-with","title":"Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net","date":"2021-08-17","arxiv_id":"2108.07851","repositories_listed":1,"syntology":null},{"url":"/paper/drb-gan-a-dynamic-resblock-generative","slug":"drb-gan-a-dynamic-resblock-generative","title":"DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer","date":"2021-08-17","arxiv_id":"2108.07379","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/drb-gan-a-dynamic-resblock-generative#ran","syntology_url":"https://syntology.ai/paper/2108.07379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07379"}},"official":null}},{"url":"/paper/an-effective-system-for-multi-format","slug":"an-effective-system-for-multi-format","title":"An Effective System for Multi-format Information Extraction","date":"2021-08-16","arxiv_id":"2108.06957","repositories_listed":1,"syntology":null},{"url":"/paper/generating-diverse-descriptions-from-semantic","slug":"generating-diverse-descriptions-from-semantic","title":"Generating Diverse Descriptions from Semantic Graphs","date":"2021-08-12","arxiv_id":"2108.05659","repositories_listed":1,"syntology":null},{"url":"/paper/handfoldingnet-a-3d-hand-pose-estimation","slug":"handfoldingnet-a-3d-hand-pose-estimation","title":"HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton","date":"2021-08-12","arxiv_id":"2108.05545","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/handfoldingnet-a-3d-hand-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/2108.05545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05545"}},"official":{"repos":["cwc1260/handfold"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mt-orl-multi-task-occlusion-relationship","slug":"mt-orl-multi-task-occlusion-relationship","title":"MT-ORL: Multi-Task Occlusion Relationship Learning","date":"2021-08-12","arxiv_id":"2108.05722","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":5,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mt-orl-multi-task-occlusion-relationship#ran","syntology_url":"https://syntology.ai/paper/2108.05722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05722"}},"official":{"repos":["fengpanhe/mt-orl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/vision-language-transformer-and-query","slug":"vision-language-transformer-and-query","title":"Vision-Language Transformer and Query Generation for Referring Segmentation","date":"2021-08-12","arxiv_id":"2108.05565","repositories_listed":1,"syntology":null},{"url":"/paper/cpnet-cross-parallel-network-for-efficient","slug":"cpnet-cross-parallel-network-for-efficient","title":"CPNet: Cross-Parallel Network for Efficient Anomaly Detection","date":"2021-08-10","arxiv_id":"2108.04454","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-enhanced-dynamic-mode","slug":"deep-learning-enhanced-dynamic-mode","title":"Deep Learning Enhanced Dynamic Mode Decomposition","date":"2021-08-10","arxiv_id":"2108.04433","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-enhanced-dynamic-mode#ran","syntology_url":"https://syntology.ai/paper/2108.04433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04433"}},"official":{"repos":["jaylago/dldmd"],"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/multi-camera-trajectory-forecasting-with","slug":"multi-camera-trajectory-forecasting-with","title":"Multi-Camera Trajectory Forecasting with Trajectory Tensors","date":"2021-08-10","arxiv_id":"2108.04694","repositories_listed":1,"syntology":null},{"url":"/paper/sunet-symmetric-undistortion-network-for","slug":"sunet-symmetric-undistortion-network-for","title":"SUNet: Symmetric Undistortion Network for Rolling Shutter Correction","date":"2021-08-10","arxiv_id":"2108.04775","repositories_listed":1,"syntology":null},{"url":"/paper/tritransnet-rgb-d-salient-object-detection","slug":"tritransnet-rgb-d-salient-object-detection","title":"TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network","date":"2021-08-09","arxiv_id":"2108.03990","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/tritransnet-rgb-d-salient-object-detection#ran","syntology_url":"https://syntology.ai/paper/2108.03990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03990"}},"official":{"repos":["liuzywen/tritransnet"],"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/discriminative-latent-semantic-graph-for","slug":"discriminative-latent-semantic-graph-for","title":"Discriminative Latent Semantic Graph for Video Captioning","date":"2021-08-08","arxiv_id":"2108.03662","repositories_listed":1,"syntology":null},{"url":"/paper/from-voxel-to-point-iou-guided-3d-object","slug":"from-voxel-to-point-iou-guided-3d-object","title":"From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder","date":"2021-08-08","arxiv_id":"2108.03648","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-model-for-zero-shot-music-source","slug":"a-unified-model-for-zero-shot-music-source","title":"A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis","date":"2021-08-07","arxiv_id":"2108.03456","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/a-unified-model-for-zero-shot-music-source#ran","syntology_url":"https://syntology.ai/paper/2108.03456","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03456"}},"official":{"repos":["kikyo-16/a-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/simpler-is-better-few-shot-semantic","slug":"simpler-is-better-few-shot-semantic","title":"Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer","date":"2021-08-06","arxiv_id":"2108.03032","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/simpler-is-better-few-shot-semantic#ran","syntology_url":"https://syntology.ai/paper/2108.03032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03032"}},"official":{"repos":["zhiheLu/CWT-for-FSS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-encoder-decoder-based-audio-captioning","slug":"an-encoder-decoder-based-audio-captioning","title":"An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning","date":"2021-08-05","arxiv_id":"2108.02752","repositories_listed":1,"syntology":null},{"url":"/paper/fast-convergence-of-detr-with-spatially-1","slug":"fast-convergence-of-detr-with-spatially-1","title":"Fast Convergence of DETR with Spatially Modulated Co-Attention","date":"2021-08-05","arxiv_id":"2108.02404","repositories_listed":1,"syntology":null},{"url":"/paper/finetuning-pretrained-transformers-into","slug":"finetuning-pretrained-transformers-into","title":"Finetuning Pretrained Transformers into Variational Autoencoders","date":"2021-08-05","arxiv_id":"2108.02446","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-reasoning-network-for-video-based","slug":"hybrid-reasoning-network-for-video-based","title":"Hybrid Reasoning Network for Video-based Commonsense Captioning","date":"2021-08-05","arxiv_id":"2108.02365","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-global-local-representations-in","slug":"unifying-global-local-representations-in","title":"Unifying Global-Local Representations in Salient Object Detection with Transformer","date":"2021-08-05","arxiv_id":"2108.02759","repositories_listed":1,"syntology":null},{"url":"/paper/question-controlled-text-aware-image","slug":"question-controlled-text-aware-image","title":"Question-controlled Text-aware Image Captioning","date":"2021-08-04","arxiv_id":"2108.02059","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-evaluation-of-end-to-end","slug":"an-empirical-evaluation-of-end-to-end","title":"An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition","date":"2021-08-03","arxiv_id":"2108.01769","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-for-autoencoder-based-end","slug":"domain-adaptation-for-autoencoder-based-end","title":"Few-Shot Domain Adaptation For End-to-End Communication","date":"2021-08-02","arxiv_id":"2108.00874","repositories_listed":1,"syntology":null},{"url":"/paper/rindnet-edge-detection-for-discontinuity-in","slug":"rindnet-edge-detection-for-discontinuity-in","title":"RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth","date":"2021-08-02","arxiv_id":"2108.00616","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/rindnet-edge-detection-for-discontinuity-in#ran","syntology_url":"https://syntology.ai/paper/2108.00616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00616"}},"official":{"repos":["MengyangPu/RINDNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-gradually-soft-multi-task-and-data","slug":"a-gradually-soft-multi-task-and-data","title":"A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-template-guided-hybrid-pointer-network-for-1","slug":"a-template-guided-hybrid-pointer-network-for-1","title":"A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/commitbert-commit-message-generation-using-1","slug":"commitbert-commit-message-generation-using-1","title":"CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ctfn-hierarchical-learning-for-multimodal","slug":"ctfn-hierarchical-learning-for-multimodal","title":"CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/engage-the-public-poll-question-generation","slug":"engage-the-public-poll-question-generation","title":"Engage the Public: Poll Question Generation for Social Media Posts","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-content-preservation-in-text-style","slug":"enhancing-content-preservation-in-text-style","title":"Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization","date":"2021-08-01","arxiv_id":"2108.00449","repositories_listed":1,"syntology":null},{"url":"/paper/is-disentanglement-enough-on-latent","slug":"is-disentanglement-enough-on-latent","title":"Is Disentanglement enough? On Latent Representations for Controllable Music Generation","date":"2021-08-01","arxiv_id":"2108.01450","repositories_listed":1,"syntology":null},{"url":"/paper/mention-flags-mf-constraining-transformer","slug":"mention-flags-mf-constraining-transformer","title":"Mention Flags (MF): Constraining Transformer-based Text Generators","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/plotcoder-hierarchical-decoding-for","slug":"plotcoder-hierarchical-decoding-for","title":"PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-based-candidate-selection-for-nmt-1","slug":"sentiment-based-candidate-selection-for-nmt-1","title":"Sentiment-based Candidate Selection for NMT","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tag-assisted-neural-machine-translation-of","slug":"tag-assisted-neural-machine-translation-of","title":"Tag Assisted Neural Machine Translation of Film Subtitles","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dadagp-a-dataset-of-tokenized-guitarpro-songs","slug":"dadagp-a-dataset-of-tokenized-guitarpro-songs","title":"DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models","date":"2021-07-30","arxiv_id":"2107.14653","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-neural-representational-decoders-for","slug":"dynamic-neural-representational-decoders-for","title":"Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation","date":"2021-07-30","arxiv_id":"2107.14428","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-neurosymbolic","slug":"unsupervised-learning-of-neurosymbolic","title":"Unsupervised Learning of Neurosymbolic Encoders","date":"2021-07-28","arxiv_id":"2107.13132","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-boundary-proposal-network-for","slug":"adaptive-boundary-proposal-network-for","title":"Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection","date":"2021-07-27","arxiv_id":"2107.12664","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/adaptive-boundary-proposal-network-for#ran","syntology_url":"https://syntology.ai/paper/2107.12664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.12664"}},"official":{"repos":["GXYM/TextBPN"],"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/exploring-sequence-feature-alignment-for","slug":"exploring-sequence-feature-alignment-for","title":"Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers","date":"2021-07-27","arxiv_id":"2107.12636","repositories_listed":1,"syntology":null},{"url":"/paper/factseg-foreground-activation-driven-small","slug":"factseg-foreground-activation-driven-small","title":"FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery","date":"2021-07-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-hyper-gan-model-for-unpaired-multi","slug":"a-unified-hyper-gan-model-for-unpaired-multi","title":"A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation","date":"2021-07-26","arxiv_id":"2107.11945","repositories_listed":1,"syntology":null},{"url":"/paper/sharp-u-net-depthwise-convolutional-network","slug":"sharp-u-net-depthwise-convolutional-network","title":"Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation","date":"2021-07-26","arxiv_id":"2107.12461","repositories_listed":1,"syntology":null},{"url":"/paper/crosslink-net-double-branch-encoder","slug":"crosslink-net-double-branch-encoder","title":"Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions","date":"2021-07-24","arxiv_id":"2107.11517","repositories_listed":1,"syntology":null},{"url":"/paper/audio-captioning-transformer","slug":"audio-captioning-transformer","title":"Audio Captioning Transformer","date":"2021-07-21","arxiv_id":"2107.09817","repositories_listed":1,"syntology":null},{"url":"/paper/multi-stream-transformers","slug":"multi-stream-transformers","title":"Multi-Stream Transformers","date":"2021-07-21","arxiv_id":"2107.10342","repositories_listed":1,"syntology":null},{"url":"/paper/ccvs-context-aware-controllable-video","slug":"ccvs-context-aware-controllable-video","title":"CCVS: Context-aware Controllable Video Synthesis","date":"2021-07-16","arxiv_id":"2107.08037","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ccvs-context-aware-controllable-video#ran","syntology_url":"https://syntology.ai/paper/2107.08037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08037"}},"official":{"repos":["16lemoing/ccvs"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/filtered-noise-shaping-for-time-domain-room","slug":"filtered-noise-shaping-for-time-domain-room","title":"Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech","date":"2021-07-15","arxiv_id":"2107.07503","repositories_listed":1,"syntology":null},{"url":"/paper/turning-tables-generating-examples-from-semi","slug":"turning-tables-generating-examples-from-semi","title":"Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills","date":"2021-07-15","arxiv_id":"2107.07261","repositories_listed":1,"syntology":null},{"url":"/paper/fetalnet-multi-task-deep-learning-framework","slug":"fetalnet-multi-task-deep-learning-framework","title":"FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements","date":"2021-07-14","arxiv_id":"2107.06943","repositories_listed":1,"syntology":null},{"url":"/paper/surgical-instruction-generation-with","slug":"surgical-instruction-generation-with","title":"Surgical Instruction Generation with Transformers","date":"2021-07-14","arxiv_id":"2107.06964","repositories_listed":1,"syntology":null},{"url":"/paper/transattunet-multi-level-attention-guided-u","slug":"transattunet-multi-level-attention-guided-u","title":"TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation","date":"2021-07-12","arxiv_id":"2107.05274","repositories_listed":1,"syntology":null},{"url":"/paper/levi-graph-amr-parser-using-heterogeneous","slug":"levi-graph-amr-parser-using-heterogeneous","title":"Levi Graph AMR Parser using Heterogeneous Attention","date":"2021-07-09","arxiv_id":"2107.04152","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-association-based-grouping-for","slug":"multi-modal-association-based-grouping-for","title":"Multi-Modal Association based Grouping for Form Structure Extraction","date":"2021-07-09","arxiv_id":"2107.04396","repositories_listed":1,"syntology":null},{"url":"/paper/unire-a-unified-label-space-for-entity","slug":"unire-a-unified-label-space-for-entity","title":"UniRE: A Unified Label Space for Entity Relation Extraction","date":"2021-07-09","arxiv_id":"2107.04292","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/unire-a-unified-label-space-for-entity#ran","syntology_url":"https://syntology.ai/paper/2107.04292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.04292"}},"official":{"repos":["Receiling/UniRE"],"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/trans4trans-efficient-transformer-for","slug":"trans4trans-efficient-transformer-for","title":"Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World","date":"2021-07-07","arxiv_id":"2107.03172","repositories_listed":1,"syntology":null},{"url":"/paper/the-sjtu-system-for-dcase2021-challenge-task","slug":"the-sjtu-system-for-dcase2021-challenge-task","title":"THE SJTU SYSTEM FOR DCASE2021 CHALLENGE TASK 6: AUDIO CAPTIONING BASED ON ENCODER PRE-TRAINING AND REINFORCEMENT LEARNING","date":"2021-07-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uacanet-uncertainty-augmented-context","slug":"uacanet-uncertainty-augmented-context","title":"UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation","date":"2021-07-06","arxiv_id":"2107.02368","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 3 unverified","sample_list":"/paper/uacanet-uncertainty-augmented-context#ran","syntology_url":"https://syntology.ai/paper/2107.02368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02368"}},"official":{"repos":["plemeri/UACANet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-delaunay-triangulation-using-self","slug":"learning-delaunay-triangulation-using-self","title":"Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain Knowledge","date":"2021-07-05","arxiv_id":"2107.01759","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-reachability-knowledge-into-a","slug":"incorporating-reachability-knowledge-into-a","title":"Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting","date":"2021-07-04","arxiv_id":"2107.01528","repositories_listed":1,"syntology":null},{"url":"/paper/neural-symbolic-solver-for-math-word-problems","slug":"neural-symbolic-solver-for-math-word-problems","title":"Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks","date":"2021-07-03","arxiv_id":"2107.01431","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":3,"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/neural-symbolic-solver-for-math-word-problems#ran","syntology_url":"https://syntology.ai/paper/2107.01431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01431"}},"official":{"repos":["QinJinghui/NS-Solver"],"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/relaxed-attention-a-simple-method-to-boost","slug":"relaxed-attention-a-simple-method-to-boost","title":"Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition","date":"2021-07-02","arxiv_id":"2107.01275","repositories_listed":1,"syntology":null},{"url":"/paper/solving-machine-learning-problems","slug":"solving-machine-learning-problems","title":"Solving Machine Learning Problems","date":"2021-07-02","arxiv_id":"2107.01238","repositories_listed":1,"syntology":null},{"url":"/paper/utnet-a-hybrid-transformer-architecture-for","slug":"utnet-a-hybrid-transformer-architecture-for","title":"UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation","date":"2021-07-02","arxiv_id":"2107.00781","repositories_listed":1,"syntology":null},{"url":"/paper/combining-frame-synchronous-and-label","slug":"combining-frame-synchronous-and-label","title":"Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition","date":"2021-07-01","arxiv_id":"2107.00764","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-target-side-inflection-in","slug":"modeling-target-side-inflection-in","title":"Modeling Target-side Inflection in Placeholder Translation","date":"2021-07-01","arxiv_id":"2107.00334","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-classification-autoencoder-to-defend","slug":"a-robust-classification-autoencoder-to-defend","title":"A Robust Classification-autoencoder to Defend Outliers and Adversaries","date":"2021-06-30","arxiv_id":"2106.15927","repositories_listed":1,"syntology":null},{"url":"/paper/dual-aspect-self-attention-based-on","slug":"dual-aspect-self-attention-based-on","title":"Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction","date":"2021-06-30","arxiv_id":"2106.15842","repositories_listed":1,"syntology":null},{"url":"/paper/hyspa-hybrid-span-generation-for-scalable","slug":"hyspa-hybrid-span-generation-for-scalable","title":"HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction","date":"2021-06-30","arxiv_id":"2106.15838","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/hyspa-hybrid-span-generation-for-scalable#ran","syntology_url":"https://syntology.ai/paper/2106.15838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.15838"}},"official":{"repos":["renll/HySPA"],"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/improving-black-box-optimization-in-vae","slug":"improving-black-box-optimization-in-vae","title":"Improving black-box optimization in VAE latent space using decoder uncertainty","date":"2021-06-30","arxiv_id":"2107.00096","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/improving-black-box-optimization-in-vae#ran","syntology_url":"https://syntology.ai/paper/2107.00096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00096"}},"official":null}},{"url":"/paper/multimodal-trajectory-prediction-conditioned","slug":"multimodal-trajectory-prediction-conditioned","title":"Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals","date":"2021-06-28","arxiv_id":"2106.15004","repositories_listed":1,"syntology":null},{"url":"/paper/effective-cascade-dual-decoder-model-for","slug":"effective-cascade-dual-decoder-model-for","title":"A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction","date":"2021-06-27","arxiv_id":"2106.14163","repositories_listed":1,"syntology":null},{"url":"/paper/learning-mesh-representations-via-binary","slug":"learning-mesh-representations-via-binary","title":"Learning Mesh Representations via Binary Space Partitioning Tree Networks","date":"2021-06-27","arxiv_id":"2106.14274","repositories_listed":1,"syntology":null},{"url":"/paper/feature-completion-for-occluded-person-re","slug":"feature-completion-for-occluded-person-re","title":"Feature Completion for Occluded Person Re-Identification","date":"2021-06-24","arxiv_id":"2106.12733","repositories_listed":1,"syntology":null},{"url":"/paper/symmetric-wasserstein-autoencoders-1","slug":"symmetric-wasserstein-autoencoders-1","title":"Symmetric Wasserstein Autoencoders","date":"2021-06-24","arxiv_id":"2106.13024","repositories_listed":1,"syntology":null}],"record_sha256":"8395720638a4b9e30c4d11b159a881882adc965a3f01a15a7066de4c9bd2b71f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}