{"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/6","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":6,"pages_in_order":104,"rows_per_page":100,"rows":[501,600],"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/5","next":"/task/decoder/papers/7","papers":[{"url":"/paper/luminance-aware-color-transform-for-multiple","slug":"luminance-aware-color-transform-for-multiple","title":"Luminance-aware Color Transform for Multiple Exposure Correction","date":"2023-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/attention-as-a-guide-for-simultaneous-speech","slug":"attention-as-a-guide-for-simultaneous-speech","title":"Attention as a Guide for Simultaneous Speech Translation","date":"2022-12-15","arxiv_id":"2212.07850","repositories_listed":2,"syntology":null},{"url":"/paper/gpvit-a-high-resolution-non-hierarchical","slug":"gpvit-a-high-resolution-non-hierarchical","title":"GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation","date":"2022-12-13","arxiv_id":"2212.06795","repositories_listed":2,"syntology":null},{"url":"/paper/learning-3d-representations-from-2d-pre","slug":"learning-3d-representations-from-2d-pre","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders","date":"2022-12-13","arxiv_id":"2212.06785","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-3d-representations-from-2d-pre#ran","syntology_url":"https://syntology.ai/paper/2212.06785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06785"}},"official":{"repos":["zrrskywalker/i2p-mae"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/causalegm-a-general-causal-inference","slug":"causalegm-a-general-causal-inference","title":"CausalEGM: a general causal inference framework by encoding generative modeling","date":"2022-12-08","arxiv_id":"2212.05925","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/causalegm-a-general-causal-inference#ran","syntology_url":"https://syntology.ai/paper/2212.05925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.05925"}},"official":{"repos":["suwonglab/causalegm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/semantic-conditional-diffusion-networks-for","slug":"semantic-conditional-diffusion-networks-for","title":"Semantic-Conditional Diffusion Networks for Image Captioning","date":"2022-12-06","arxiv_id":"2212.03099","repositories_listed":2,"syntology":null},{"url":"/paper/box2mask-box-supervised-instance-segmentation","slug":"box2mask-box-supervised-instance-segmentation","title":"Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution","date":"2022-12-03","arxiv_id":"2212.01579","repositories_listed":2,"syntology":null},{"url":"/paper/uperformer-a-multi-scale-transformer-based","slug":"uperformer-a-multi-scale-transformer-based","title":"MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation","date":"2022-11-25","arxiv_id":"2211.13928","repositories_listed":2,"syntology":null},{"url":"/paper/harim-evaluating-summary-quality-with","slug":"harim-evaluating-summary-quality-with","title":"HaRiM$^+$: Evaluating Summary Quality with Hallucination Risk","date":"2022-11-22","arxiv_id":"2211.12118","repositories_listed":2,"syntology":null},{"url":"/paper/uni-perceiver-v2-a-generalist-model-for-large","slug":"uni-perceiver-v2-a-generalist-model-for-large","title":"Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks","date":"2022-11-17","arxiv_id":"2211.09808","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uni-perceiver-v2-a-generalist-model-for-large#ran","syntology_url":"https://syntology.ai/paper/2211.09808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09808"}},"official":{"repos":["fundamentalvision/Uni-Perceiver"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/magnet-a-graph-u-net-architecture-for-mesh","slug":"magnet-a-graph-u-net-architecture-for-mesh","title":"MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations","date":"2022-11-01","arxiv_id":"2211.00713","repositories_listed":2,"syntology":null},{"url":"/paper/amgnet-multi-scale-graph-neural-networks-for","slug":"amgnet-multi-scale-graph-neural-networks-for","title":"AMGNET: multi-scale graph neural networks for flow field prediction","date":"2022-10-13","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/feature-proxy-transformer-for-few-shot","slug":"feature-proxy-transformer-for-few-shot","title":"Feature-Proxy Transformer for Few-Shot Segmentation","date":"2022-10-13","arxiv_id":"2210.06908","repositories_listed":2,"syntology":null},{"url":"/paper/speechut-bridging-speech-and-text-with-hidden","slug":"speechut-bridging-speech-and-text-with-hidden","title":"SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training","date":"2022-10-07","arxiv_id":"2210.03730","repositories_listed":2,"syntology":null},{"url":"/paper/phenaki-variable-length-video-generation-from","slug":"phenaki-variable-length-video-generation-from","title":"Phenaki: Variable Length Video Generation From Open Domain Textual Description","date":"2022-10-05","arxiv_id":"2210.02399","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/phenaki-variable-length-video-generation-from#ran","syntology_url":"https://syntology.ai/paper/2210.02399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02399"}},"official":null}},{"url":"/paper/wikihan-a-new-comparative-dataset-for-chinese","slug":"wikihan-a-new-comparative-dataset-for-chinese","title":"WikiHan: A New Comparative Dataset for Chinese Languages","date":"2022-10-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/an-equal-size-hard-em-algorithm-for-diverse","slug":"an-equal-size-hard-em-algorithm-for-diverse","title":"An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation","date":"2022-09-29","arxiv_id":"2209.14627","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/an-equal-size-hard-em-algorithm-for-diverse#ran","syntology_url":"https://syntology.ai/paper/2209.14627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14627"}},"official":{"repos":["anonymous-1759/eqhard-em","manga-uofa/eqhard-em"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/make-a-video-text-to-video-generation-without","slug":"make-a-video-text-to-video-generation-without","title":"Make-A-Video: Text-to-Video Generation without Text-Video Data","date":"2022-09-29","arxiv_id":"2209.14792","repositories_listed":2,"syntology":null},{"url":"/paper/sapa-similarity-aware-point-affiliation-for","slug":"sapa-similarity-aware-point-affiliation-for","title":"SAPA: Similarity-Aware Point Affiliation for Feature Upsampling","date":"2022-09-26","arxiv_id":"2209.12866","repositories_listed":2,"syntology":null},{"url":"/paper/cmgan-conformer-based-metric-gan-for-monaural","slug":"cmgan-conformer-based-metric-gan-for-monaural","title":"CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement","date":"2022-09-22","arxiv_id":"2209.11112","repositories_listed":2,"syntology":null},{"url":"/paper/movq-modulating-quantized-vectors-for-high","slug":"movq-modulating-quantized-vectors-for-high","title":"MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation","date":"2022-09-19","arxiv_id":"2209.09002","repositories_listed":2,"syntology":null},{"url":"/paper/hardnet-dfus-an-enhanced-harmonically","slug":"hardnet-dfus-an-enhanced-harmonically","title":"HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation","date":"2022-09-15","arxiv_id":"2209.07313","repositories_listed":2,"syntology":null},{"url":"/paper/unified-fully-and-timestamp-supervised","slug":"unified-fully-and-timestamp-supervised","title":"Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation","date":"2022-09-01","arxiv_id":"2209.00638","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/unified-fully-and-timestamp-supervised#ran","syntology_url":"https://syntology.ai/paper/2209.00638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.00638"}},"official":{"repos":["boschresearch/uvast"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deepinteraction-3d-object-detection-via","slug":"deepinteraction-3d-object-detection-via","title":"DeepInteraction: 3D Object Detection via Modality Interaction","date":"2022-08-23","arxiv_id":"2208.11112","repositories_listed":2,"syntology":null},{"url":"/paper/contextual-mask-auto-encoder-for-dense","slug":"contextual-mask-auto-encoder-for-dense","title":"ConTextual Masked Auto-Encoder for Dense Passage Retrieval","date":"2022-08-16","arxiv_id":"2208.07670","repositories_listed":2,"syntology":null},{"url":"/paper/triple-view-feature-learning-for-medical","slug":"triple-view-feature-learning-for-medical","title":"Triple-View Feature Learning for Medical Image Segmentation","date":"2022-08-12","arxiv_id":"2208.06303","repositories_listed":2,"syntology":null},{"url":"/paper/abstractive-meeting-summarization-a-survey","slug":"abstractive-meeting-summarization-a-survey","title":"Abstractive Meeting Summarization: A Survey","date":"2022-08-08","arxiv_id":"2208.04163","repositories_listed":2,"syntology":null},{"url":"/paper/frozen-clip-models-are-efficient-video","slug":"frozen-clip-models-are-efficient-video","title":"Frozen CLIP Models are Efficient Video Learners","date":"2022-08-06","arxiv_id":"2208.03550","repositories_listed":2,"syntology":{"n":10,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"5 ran (of which 3 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) · 5 unverified","sample_list":"/paper/frozen-clip-models-are-efficient-video#ran","syntology_url":"https://syntology.ai/paper/2208.03550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03550"}},"official":{"repos":["opengvlab/efficient-video-recognition"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-networks-for-channel-decoding","slug":"graph-neural-networks-for-channel-decoding","title":"Graph Neural Networks for Channel Decoding","date":"2022-07-29","arxiv_id":"2207.14742","repositories_listed":2,"syntology":null},{"url":"/paper/group-detr-fast-training-convergence-with","slug":"group-detr-fast-training-convergence-with","title":"Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment","date":"2022-07-26","arxiv_id":"2207.13085","repositories_listed":2,"syntology":null},{"url":"/paper/improving-mandarin-speech-recogntion-with","slug":"improving-mandarin-speech-recogntion-with","title":"Improving Mandarin Speech Recogntion with Block-augmented Transformer","date":"2022-07-24","arxiv_id":"2207.11697","repositories_listed":2,"syntology":null},{"url":"/paper/neural-color-operators-for-sequential-image","slug":"neural-color-operators-for-sequential-image","title":"Neural Color Operators for Sequential Image Retouching","date":"2022-07-17","arxiv_id":"2207.08080","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 3 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) · 0 unverified","sample_list":"/paper/neural-color-operators-for-sequential-image#ran","syntology_url":"https://syntology.ai/paper/2207.08080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08080"}},"official":{"repos":["amberwangyili/neurop"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generalizable-memory-driven-transformer-for","slug":"generalizable-memory-driven-transformer-for","title":"Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System","date":"2022-07-16","arxiv_id":"2207.07827","repositories_listed":2,"syntology":null},{"url":"/paper/comer-modeling-coverage-for-transformer-based","slug":"comer-modeling-coverage-for-transformer-based","title":"CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition","date":"2022-07-10","arxiv_id":"2207.04410","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/comer-modeling-coverage-for-transformer-based#ran","syntology_url":"https://syntology.ai/paper/2207.04410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04410"}},"official":{"repos":["Green-Wood/CoMER"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/coderl-mastering-code-generation-through","slug":"coderl-mastering-code-generation-through","title":"CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning","date":"2022-07-05","arxiv_id":"2207.01780","repositories_listed":2,"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/coderl-mastering-code-generation-through#ran","syntology_url":"https://syntology.ai/paper/2207.01780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01780"}},"official":{"repos":["salesforce/coderl"],"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/temporal-attention-unit-towards-efficient","slug":"temporal-attention-unit-towards-efficient","title":"Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning","date":"2022-06-24","arxiv_id":"2206.12126","repositories_listed":2,"syntology":null},{"url":"/paper/scaling-autoregressive-models-for-content","slug":"scaling-autoregressive-models-for-content","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","date":"2022-06-22","arxiv_id":"2206.10789","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scaling-autoregressive-models-for-content#ran","syntology_url":"https://syntology.ai/paper/2206.10789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10789"}},"official":null}},{"url":"/paper/paraformer-fast-and-accurate-parallel","slug":"paraformer-fast-and-accurate-parallel","title":"Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition","date":"2022-06-16","arxiv_id":"2206.08317","repositories_listed":2,"syntology":null},{"url":"/paper/efficient-decoder-free-object-detection-with","slug":"efficient-decoder-free-object-detection-with","title":"Efficient Decoder-free Object Detection with Transformers","date":"2022-06-14","arxiv_id":"2206.06829","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-decoder-free-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2206.06829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06829"}},"official":{"repos":["Pealing/DFFT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/automatic-polyp-segmentation-with-multiple","slug":"automatic-polyp-segmentation-with-multiple","title":"Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network","date":"2022-06-13","arxiv_id":"2206.06264","repositories_listed":2,"syntology":null},{"url":"/paper/ifrnet-intermediate-feature-refine-network","slug":"ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","arxiv_id":"2205.14620","repositories_listed":2,"syntology":{"n":20,"n_ran":17,"n_constructed":2,"n_ran_checked":13,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"17 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ifrnet-intermediate-feature-refine-network#ran","syntology_url":"https://syntology.ai/paper/2205.14620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14620"}},"official":{"repos":["ltkong218/ifrnet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/other-roles-matter-enhancing-role-oriented-1","slug":"other-roles-matter-enhancing-role-oriented-1","title":"Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions","date":"2022-05-26","arxiv_id":"2205.13190","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/other-roles-matter-enhancing-role-oriented-1#ran","syntology_url":"https://syntology.ai/paper/2205.13190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13190"}},"official":{"repos":["atulkum/pointer_summarizer","xiaolinandy/rods"],"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/deep-discriminative-direct-decoders-for-high","slug":"deep-discriminative-direct-decoders-for-high","title":"Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis","date":"2022-05-22","arxiv_id":"2205.10947","repositories_listed":2,"syntology":null},{"url":"/paper/arbitrary-shape-text-detection-via-boundary","slug":"arbitrary-shape-text-detection-via-boundary","title":"Arbitrary Shape Text Detection via Boundary Transformer","date":"2022-05-11","arxiv_id":"2205.05320","repositories_listed":2,"syntology":null},{"url":"/paper/dynamic-focus-aware-positional-queries-for","slug":"dynamic-focus-aware-positional-queries-for","title":"Dynamic Focus-aware Positional Queries for Semantic Segmentation","date":"2022-04-04","arxiv_id":"2204.01244","repositories_listed":2,"syntology":null},{"url":"/paper/binsformer-revisiting-adaptive-bins-for","slug":"binsformer-revisiting-adaptive-bins-for","title":"BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation","date":"2022-04-03","arxiv_id":"2204.00987","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/binsformer-revisiting-adaptive-bins-for#ran","syntology_url":"https://syntology.ai/paper/2204.00987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00987"}},"official":{"repos":["zhyever/monocular-depth-estimation-toolbox"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mae-ast-masked-autoencoding-audio-spectrogram","slug":"mae-ast-masked-autoencoding-audio-spectrogram","title":"MAE-AST: Masked Autoencoding Audio Spectrogram Transformer","date":"2022-03-30","arxiv_id":"2203.16691","repositories_listed":2,"syntology":null},{"url":"/paper/end-to-end-transformer-based-model-for-image","slug":"end-to-end-transformer-based-model-for-image","title":"End-to-End Transformer Based Model for Image Captioning","date":"2022-03-29","arxiv_id":"2203.15350","repositories_listed":2,"syntology":{"n":33,"n_ran":17,"n_constructed":9,"n_ran_checked":14,"n_instrument":3,"n_unverified":16,"n_honours":1,"n_violates":2,"n_no_contract":11,"n_pointer_only":15,"phrase":"17 ran (of which 9 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 2 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/end-to-end-transformer-based-model-for-image#ran","syntology_url":"https://syntology.ai/paper/2203.15350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15350"}},"official":{"repos":["232525/PureT"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mfsnet-a-multi-focus-segmentation-network-for","slug":"mfsnet-a-multi-focus-segmentation-network-for","title":"MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation","date":"2022-03-27","arxiv_id":"2203.14341","repositories_listed":2,"syntology":null},{"url":"/paper/improving-anatomical-plausibility-in-medical","slug":"improving-anatomical-plausibility-in-medical","title":"Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis","date":"2022-03-21","arxiv_id":"2203.10977","repositories_listed":2,"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/improving-anatomical-plausibility-in-medical#ran","syntology_url":"https://syntology.ai/paper/2203.10977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10977"}},"official":{"repos":["ngaggion/HybridGNet","ngaggion/chest-xray-landmark-dataset"],"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/robust-visual-tracking-by-segmentation","slug":"robust-visual-tracking-by-segmentation","title":"Robust Visual Tracking by Segmentation","date":"2022-03-21","arxiv_id":"2203.11191","repositories_listed":2,"syntology":null},{"url":"/paper/futr3d-a-unified-sensor-fusion-framework-for","slug":"futr3d-a-unified-sensor-fusion-framework-for","title":"FUTR3D: A Unified Sensor Fusion Framework for 3D Detection","date":"2022-03-20","arxiv_id":"2203.10642","repositories_listed":2,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/futr3d-a-unified-sensor-fusion-framework-for#ran","syntology_url":"https://syntology.ai/paper/2203.10642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10642"}},"official":null}},{"url":"/paper/the-devil-is-in-the-details-window-based","slug":"the-devil-is-in-the-details-window-based","title":"The Devil Is in the Details: Window-based Attention for Image Compression","date":"2022-03-16","arxiv_id":"2203.08450","repositories_listed":2,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":11,"n_instrument":4,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 3 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-devil-is-in-the-details-window-based#ran","syntology_url":"https://syntology.ai/paper/2203.08450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08450"}},"official":{"repos":["googolxx/stf"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/phtrans-parallelly-aggregating-global-and","slug":"phtrans-parallelly-aggregating-global-and","title":"PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation","date":"2022-03-09","arxiv_id":"2203.04568","repositories_listed":2,"syntology":null},{"url":"/paper/unext-mlp-based-rapid-medical-image","slug":"unext-mlp-based-rapid-medical-image","title":"UNeXt: MLP-based Rapid Medical Image Segmentation Network","date":"2022-03-09","arxiv_id":"2203.04967","repositories_listed":2,"syntology":{"n":26,"n_ran":20,"n_constructed":0,"n_ran_checked":17,"n_instrument":3,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":16,"n_pointer_only":6,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 0 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/unext-mlp-based-rapid-medical-image#ran","syntology_url":"https://syntology.ai/paper/2203.04967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04967"}},"official":{"repos":["jeya-maria-jose/unext-pytorch"],"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":["listed","official"]}}},{"url":"/paper/unixcoder-unified-cross-modal-pre-training","slug":"unixcoder-unified-cross-modal-pre-training","title":"UniXcoder: Unified Cross-Modal Pre-training for Code Representation","date":"2022-03-08","arxiv_id":"2203.03850","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/unixcoder-unified-cross-modal-pre-training#ran","syntology_url":"https://syntology.ai/paper/2203.03850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03850"}},"official":{"repos":["microsoft/CodeBERT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/syntax-aware-network-for-handwritten","slug":"syntax-aware-network-for-handwritten","title":"Syntax-Aware Network for Handwritten Mathematical Expression Recognition","date":"2022-03-03","arxiv_id":"2203.01601","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/syntax-aware-network-for-handwritten#ran","syntology_url":"https://syntology.ai/paper/2203.01601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01601"}},"official":{"repos":["tal-tech/san","phymond/hme100k"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/ctformer-convolution-free-token2token-dilated","slug":"ctformer-convolution-free-token2token-dilated","title":"CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising","date":"2022-02-28","arxiv_id":"2202.13517","repositories_listed":2,"syntology":null},{"url":"/paper/the-impact-of-lexical-and-grammatical-1","slug":"the-impact-of-lexical-and-grammatical-1","title":"The impact of lexical and grammatical processing on generating code from natural language","date":"2022-02-28","arxiv_id":"2202.13972","repositories_listed":2,"syntology":null},{"url":"/paper/blind-image-super-resolution-with-semantic","slug":"blind-image-super-resolution-with-semantic","title":"Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors","date":"2022-02-26","arxiv_id":"2202.13142","repositories_listed":2,"syntology":null},{"url":"/paper/retriever-learning-content-style-1","slug":"retriever-learning-content-style-1","title":"Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph","date":"2022-02-24","arxiv_id":"2202.12307","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"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 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) · 0 unverified","sample_list":"/paper/retriever-learning-content-style-1#ran","syntology_url":"https://syntology.ai/paper/2202.12307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.12307"}},"official":{"repos":["xrenaa/Retriever"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/transformer-and-cnn-hybrid-deep-neural","slug":"transformer-and-cnn-hybrid-deep-neural","title":"Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery","date":"2022-01-19","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/scrolls-standardized-comparison-over-long","slug":"scrolls-standardized-comparison-over-long","title":"SCROLLS: Standardized CompaRison Over Long Language Sequences","date":"2022-01-10","arxiv_id":"2201.03533","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scrolls-standardized-comparison-over-long#ran","syntology_url":"https://syntology.ai/paper/2201.03533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03533"}},"official":{"repos":["tau-nlp/scrolls"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cost-aggregation-is-all-you-need-for-few-shot","slug":"cost-aggregation-is-all-you-need-for-few-shot","title":"Cost Aggregation Is All You Need for Few-Shot Segmentation","date":"2021-12-22","arxiv_id":"2112.11685","repositories_listed":2,"syntology":null},{"url":"/paper/transmef-a-transformer-based-multi-exposure","slug":"transmef-a-transformer-based-multi-exposure","title":"TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning","date":"2021-12-02","arxiv_id":"2112.01030","repositories_listed":2,"syntology":null},{"url":"/paper/mismatch-learning-to-change-predictive","slug":"mismatch-learning-to-change-predictive","title":"MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels","date":"2021-10-23","arxiv_id":"2110.12179","repositories_listed":2,"syntology":null},{"url":"/paper/self-validation-early-stopping-for-single","slug":"self-validation-early-stopping-for-single","title":"Self-Validation: Early Stopping for Single-Instance Deep Generative Priors","date":"2021-10-23","arxiv_id":"2110.12271","repositories_listed":2,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/self-validation-early-stopping-for-single#ran","syntology_url":"https://syntology.ai/paper/2110.12271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.12271"}},"official":null}},{"url":"/paper/planerecnet-multi-task-learning-with-cross","slug":"planerecnet-multi-task-learning-with-cross","title":"PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image","date":"2021-10-21","arxiv_id":"2110.11219","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/planerecnet-multi-task-learning-with-cross#ran","syntology_url":"https://syntology.ai/paper/2110.11219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11219"}},"official":{"repos":["eryixie/planerecnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-deep-generative-decoder-using-map-1","slug":"the-deep-generative-decoder-using-map-1","title":"The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data","date":"2021-10-13","arxiv_id":"2110.06672","repositories_listed":2,"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":0,"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/the-deep-generative-decoder-using-map-1#ran","syntology_url":"https://syntology.ai/paper/2110.06672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06672"}},"official":{"repos":["Center-for-Health-Data-Science/scDGD"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/k-wav2vec-2-0-automatic-speech-recognition","slug":"k-wav2vec-2-0-automatic-speech-recognition","title":"K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables","date":"2021-10-11","arxiv_id":"2110.05172","repositories_listed":2,"syntology":null},{"url":"/paper/optimized-u-net-for-brain-tumor-segmentation","slug":"optimized-u-net-for-brain-tumor-segmentation","title":"Optimized U-Net for Brain Tumor Segmentation","date":"2021-10-07","arxiv_id":"2110.03352","repositories_listed":2,"syntology":null},{"url":"/paper/double-encoder-decoder-networks-for","slug":"double-encoder-decoder-networks-for","title":"Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation","date":"2021-10-05","arxiv_id":"2110.01939","repositories_listed":2,"syntology":null},{"url":"/paper/deep-contextual-video-compression","slug":"deep-contextual-video-compression","title":"Deep Contextual Video Compression","date":"2021-09-30","arxiv_id":"2109.15047","repositories_listed":2,"syntology":{"n":21,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/deep-contextual-video-compression#ran","syntology_url":"https://syntology.ai/paper/2109.15047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.15047"}},"official":{"repos":["DeepMC-DCVC/DCVC"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-embedded-k-means-clustering","slug":"deep-embedded-k-means-clustering","title":"Deep Embedded K-Means Clustering","date":"2021-09-30","arxiv_id":"2109.15149","repositories_listed":2,"syntology":null},{"url":"/paper/the-niutrans-system-for-wngt-2020-efficiency-1","slug":"the-niutrans-system-for-wngt-2020-efficiency-1","title":"The NiuTrans System for WNGT 2020 Efficiency Task","date":"2021-09-16","arxiv_id":"2109.08008","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-niutrans-system-for-wngt-2020-efficiency-1#ran","syntology_url":"https://syntology.ai/paper/2109.08008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.08008"}},"official":{"repos":["NiuTrans/NiuTrans.NMT","niutrans/niutensor"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/carnet-a-lightweight-and-efficient-encoder","slug":"carnet-a-lightweight-and-efficient-encoder","title":"Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection","date":"2021-09-13","arxiv_id":"2109.05707","repositories_listed":2,"syntology":null},{"url":"/paper/image-compression-with-recurrent-neural-1","slug":"image-compression-with-recurrent-neural-1","title":"Image Compression with Recurrent Neural Network and Generalized Divisive Normalization","date":"2021-09-05","arxiv_id":"2109.01999","repositories_listed":2,"syntology":null},{"url":"/paper/lot-a-benchmark-for-evaluating-chinese-long","slug":"lot-a-benchmark-for-evaluating-chinese-long","title":"LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation","date":"2021-08-30","arxiv_id":"2108.12960","repositories_listed":2,"syntology":null},{"url":"/paper/enhanced-seq2seq-autoencoder-via-contrastive","slug":"enhanced-seq2seq-autoencoder-via-contrastive","title":"Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization","date":"2021-08-26","arxiv_id":"2108.11992","repositories_listed":2,"syntology":null},{"url":"/paper/sentence-t5-scalable-sentence-encoders-from","slug":"sentence-t5-scalable-sentence-encoders-from","title":"Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models","date":"2021-08-19","arxiv_id":"2108.08877","repositories_listed":2,"syntology":null},{"url":"/paper/x-modaler-a-versatile-and-high-performance","slug":"x-modaler-a-versatile-and-high-performance","title":"X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics","date":"2021-08-18","arxiv_id":"2108.08217","repositories_listed":2,"syntology":null},{"url":"/paper/polyp-pvt-polyp-segmentation-with-pyramid","slug":"polyp-pvt-polyp-segmentation-with-pyramid","title":"Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers","date":"2021-08-16","arxiv_id":"2108.06932","repositories_listed":2,"syntology":null},{"url":"/paper/tiny-neural-models-for-seq2seq","slug":"tiny-neural-models-for-seq2seq","title":"Tiny Neural Models for Seq2Seq","date":"2021-08-07","arxiv_id":"2108.03340","repositories_listed":2,"syntology":null},{"url":"/paper/document-level-event-extraction-via-parallel","slug":"document-level-event-extraction-via-parallel","title":"Document-level Event Extraction via Parallel Prediction Networks","date":"2021-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/spatial-temporal-transformer-for-dynamic","slug":"spatial-temporal-transformer-for-dynamic","title":"Spatial-Temporal Transformer for Dynamic Scene Graph Generation","date":"2021-07-26","arxiv_id":"2107.12309","repositories_listed":2,"syntology":{"n":14,"n_ran":13,"n_constructed":4,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":6,"phrase":"13 ran (of which 4 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/spatial-temporal-transformer-for-dynamic#ran","syntology_url":"https://syntology.ai/paper/2107.12309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.12309"}},"official":{"repos":["yrcong/sttran"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/cl4ac-a-contrastive-loss-for-audio-captioning","slug":"cl4ac-a-contrastive-loss-for-audio-captioning","title":"CL4AC: A Contrastive Loss for Audio Captioning","date":"2021-07-21","arxiv_id":"2107.09990","repositories_listed":2,"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/cl4ac-a-contrastive-loss-for-audio-captioning#ran","syntology_url":"https://syntology.ai/paper/2107.09990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09990"}},"official":{"repos":["liuxubo717/cl4ac","liuxubo717/contrastive_loss_for_audio_captioning"],"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/levit-unet-make-faster-encoders-with","slug":"levit-unet-make-faster-encoders-with","title":"LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation","date":"2021-07-19","arxiv_id":"2107.08623","repositories_listed":2,"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/levit-unet-make-faster-encoders-with#ran","syntology_url":"https://syntology.ai/paper/2107.08623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08623"}},"official":{"repos":["apple1986/LeViT_UNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/sequence-to-sequence-piano-transcription-with","slug":"sequence-to-sequence-piano-transcription-with","title":"Sequence-to-Sequence Piano Transcription with Transformers","date":"2021-07-19","arxiv_id":"2107.09142","repositories_listed":2,"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/sequence-to-sequence-piano-transcription-with#ran","syntology_url":"https://syntology.ai/paper/2107.09142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09142"}},"official":null}},{"url":"/paper/the-piano-inpainting-application","slug":"the-piano-inpainting-application","title":"The Piano Inpainting Application","date":"2021-07-13","arxiv_id":"2107.05944","repositories_listed":2,"syntology":null},{"url":"/paper/visual-parser-representing-part-whole","slug":"visual-parser-representing-part-whole","title":"Visual Parser: Representing Part-whole Hierarchies with Transformers","date":"2021-07-13","arxiv_id":"2107.05790","repositories_listed":2,"syntology":null},{"url":"/paper/long-short-term-transformer-for-online-action","slug":"long-short-term-transformer-for-online-action","title":"Long Short-Term Transformer for Online Action Detection","date":"2021-07-07","arxiv_id":"2107.03377","repositories_listed":2,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/long-short-term-transformer-for-online-action#ran","syntology_url":"https://syntology.ai/paper/2107.03377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.03377"}},"official":{"repos":["amazon-research/long-short-term-transformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/opt-omni-perception-pre-trainer-for-cross","slug":"opt-omni-perception-pre-trainer-for-cross","title":"OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation","date":"2021-07-01","arxiv_id":"2107.00249","repositories_listed":2,"syntology":null},{"url":"/paper/deltalm-encoder-decoder-pre-training-for","slug":"deltalm-encoder-decoder-pre-training-for","title":"DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders","date":"2021-06-25","arxiv_id":"2106.13736","repositories_listed":2,"syntology":null},{"url":"/paper/feature-alignment-for-approximated","slug":"feature-alignment-for-approximated","title":"Feature Alignment as a Generative Process","date":"2021-06-23","arxiv_id":"2106.12562","repositories_listed":2,"syntology":null},{"url":"/paper/abcd-a-graph-framework-to-convert-complex","slug":"abcd-a-graph-framework-to-convert-complex","title":"ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences","date":"2021-06-22","arxiv_id":"2106.12027","repositories_listed":2,"syntology":null},{"url":"/paper/cpm-2-large-scale-cost-effective-pre-trained","slug":"cpm-2-large-scale-cost-effective-pre-trained","title":"CPM-2: Large-scale Cost-effective Pre-trained Language Models","date":"2021-06-20","arxiv_id":"2106.10715","repositories_listed":2,"syntology":null},{"url":"/paper/ted-net-convolution-free-t2t-vision","slug":"ted-net-convolution-free-t2t-vision","title":"TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising","date":"2021-06-08","arxiv_id":"2106.04650","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":4,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 4 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ted-net-convolution-free-t2t-vision#ran","syntology_url":"https://syntology.ai/paper/2106.04650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04650"}},"official":null}},{"url":"/paper/transformer-based-deep-image-matching-for","slug":"transformer-based-deep-image-matching-for","title":"TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification","date":"2021-05-30","arxiv_id":"2105.14432","repositories_listed":2,"syntology":{"n":21,"n_ran":13,"n_constructed":4,"n_ran_checked":9,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"13 ran (of which 4 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/transformer-based-deep-image-matching-for#ran","syntology_url":"https://syntology.ai/paper/2105.14432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14432"}},"official":{"repos":["shengcailiao/QAConv","ShengcaiLiao/TransMatcher"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["found_in_text","official","unlocated"]}}},{"url":"/paper/localization-of-facial-images-manipulation-in","slug":"localization-of-facial-images-manipulation-in","title":"Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks","date":"2021-05-28","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/fccdn-feature-constraint-network-for-vhr","slug":"fccdn-feature-constraint-network-for-vhr","title":"FCCDN: Feature Constraint Network for VHR Image Change Detection","date":"2021-05-23","arxiv_id":"2105.10860","repositories_listed":2,"syntology":null}],"record_sha256":"fd16a869e15b5944195801f6f404178bf53ba76ebd55124a29b9870f4fd2e34c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}