{"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":"/method/position-wise-feed-forward-layer/papers/109","list_of":"/method/position-wise-feed-forward-layer","method":"Position-Wise Feed-Forward Layer","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":109,"pages_in_order":139,"rows_per_page":100,"rows":[10801,10900],"of":13895,"counts":{"archive_papers_tagged":13895,"with_a_code_link":6514,"where_syntology_ran_a_sample":2229,"not_listed_spam_title":0,"listed":13895,"listed_where_code_ran":2229,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1902,"every_run_a_failure_of_syntologys_instrument":327,"listed_with_a_run_with_no_instrument_failure":1902,"listed_every_run_a_failure_of_syntologys_instrument":327,"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":"/method/position-wise-feed-forward-layer","prev":"/method/position-wise-feed-forward-layer/papers/108","next":"/method/position-wise-feed-forward-layer/papers/110","papers":[{"paper":"/paper/transbtsv2-wider-instead-of-deeper","slug":"transbtsv2-wider-instead-of-deeper","title":"TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images","date":"2022-01-30","arxiv_id":"2201.12785","n_code_links":2,"syntology":null},{"paper":null,"slug":"autodistil-few-shot-task-agnostic-neural","title":"AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models","date":"2022-01-29","arxiv_id":"2201.12507","n_code_links":0,"syntology":null},{"paper":"/paper/decepticons-corrupted-transformers-breach","slug":"decepticons-corrupted-transformers-breach","title":"Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models","date":"2022-01-29","arxiv_id":"2201.12675","n_code_links":1,"syntology":null},{"paper":null,"slug":"research-on-patch-attentive-neural-process","title":"Research on Patch Attentive Neural Process","date":"2022-01-29","arxiv_id":"2202.01884","n_code_links":0,"syntology":null},{"paper":null,"slug":"rewiring-with-positional-encodings-for-graph","title":"Rewiring with Positional Encodings for Graph Neural Networks","date":"2022-01-29","arxiv_id":"2201.12674","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-robustness-of-3d-point-cloud","slug":"benchmarking-robustness-of-3d-point-cloud","title":"Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions","date":"2022-01-28","arxiv_id":"2201.12296","n_code_links":6,"syntology":{"ran":24,"of":29,"n_ran_checked":16,"n_instrument":8,"unverified":5,"pointer_only":7,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 0 violated, 14 with no contract checked; 8 where Syntology's instrument failed) · 5 unverified","official":{"repos":["jiachens/ModelNet40-C"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/can-wikipedia-help-offline-reinforcement","slug":"can-wikipedia-help-offline-reinforcement","title":"Can Wikipedia Help Offline Reinforcement Learning?","date":"2022-01-28","arxiv_id":"2201.12122","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["machelreid/can-wikipedia-help-offline-rl"],"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"]}}},{"paper":"/paper/dab-detr-dynamic-anchor-boxes-are-better-1","slug":"dab-detr-dynamic-anchor-boxes-are-better-1","title":"DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR","date":"2022-01-28","arxiv_id":"2201.12329","n_code_links":8,"syntology":{"ran":6,"of":11,"n_ran_checked":4,"n_instrument":2,"unverified":5,"pointer_only":0,"phrase":"6 ran (of which 3 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) · 5 unverified","official":{"repos":["slongliu/dab-detr"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"o-vit-orthogonal-vision-transformer","title":"O-ViT: Orthogonal Vision Transformer","date":"2022-01-28","arxiv_id":"2201.12133","n_code_links":0,"syntology":null},{"paper":"/paper/vrt-a-video-restoration-transformer","slug":"vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","arxiv_id":"2201.12288","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":0,"n_instrument":4,"unverified":1,"pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jingyunliang/vrt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/generalised-image-outpainting-with-u","slug":"generalised-image-outpainting-with-u","title":"Generalised Image Outpainting with U-Transformer","date":"2022-01-27","arxiv_id":"2201.11403","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-module-networks-for-systematic","slug":"transformer-module-networks-for-systematic","title":"Transformer Module Networks for Systematic Generalization in Visual Question Answering","date":"2022-01-27","arxiv_id":"2201.11316","n_code_links":1,"syntology":null},{"paper":null,"slug":"dnnfuser-generative-pre-trained-transformer","title":"DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators","date":"2022-01-26","arxiv_id":"2201.11218","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-decoder-transformer-for-end-to-end","title":"On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR","date":"2022-01-26","arxiv_id":"2201.10792","n_code_links":0,"syntology":null},{"paper":"/paper/dual-tasks-siamese-transformer-framework-for","slug":"dual-tasks-siamese-transformer-framework-for","title":"Dual-Tasks Siamese Transformer Framework for Building Damage Assessment","date":"2022-01-26","arxiv_id":"2201.10953","n_code_links":0,"syntology":null},{"paper":"/paper/neural-grapheme-to-phoneme-conversion-with","slug":"neural-grapheme-to-phoneme-conversion-with","title":"Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models","date":"2022-01-26","arxiv_id":"2201.10716","n_code_links":1,"syntology":null},{"paper":"/paper/predicting-knee-osteoarthritis-progression","slug":"predicting-knee-osteoarthritis-progression","title":"Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning","date":"2022-01-26","arxiv_id":"2201.10849","n_code_links":1,"syntology":null},{"paper":null,"slug":"transppg-two-stream-transformer-for-remote","title":"TransPPG: Two-stream Transformer for Remote Heart Rate Estimate","date":"2022-01-26","arxiv_id":"2201.10873","n_code_links":0,"syntology":null},{"paper":"/paper/when-shift-operation-meets-vision-transformer","slug":"when-shift-operation-meets-vision-transformer","title":"When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism","date":"2022-01-26","arxiv_id":"2201.10801","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","official":{"repos":["microsoft/SPACH"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/convolutional-xformers-for-vision","slug":"convolutional-xformers-for-vision","title":"Convolutional Xformers for Vision","date":"2022-01-25","arxiv_id":"2201.10271","n_code_links":1,"syntology":null},{"paper":"/paper/explore-and-match-end-to-end-video-grounding","slug":"explore-and-match-end-to-end-video-grounding","title":"Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos","date":"2022-01-25","arxiv_id":"2201.10168","n_code_links":1,"syntology":null},{"paper":null,"slug":"masked-transformer-for-neighhourhood-aware","title":"Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer","date":"2022-01-25","arxiv_id":"2201.13311","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-transport-based-data-augmentation-for","title":"Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation","date":"2022-01-25","arxiv_id":"2202.00567","n_code_links":0,"syntology":null},{"paper":"/paper/vit-hgr-vision-transformer-based-hand-gesture","slug":"vit-hgr-vision-transformer-based-hand-gesture","title":"ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals","date":"2022-01-25","arxiv_id":"2201.10060","n_code_links":1,"syntology":null},{"paper":null,"slug":"zero-shot-sketch-based-image-retrieval-using","title":"Zero-Shot Sketch Based Image Retrieval using Graph Transformer","date":"2022-01-25","arxiv_id":"2201.10185","n_code_links":0,"syntology":null},{"paper":"/paper/improving-chest-x-ray-report-generation-by","slug":"improving-chest-x-ray-report-generation-by","title":"Improving Chest X-Ray Report Generation by Leveraging Warm Starting","date":"2022-01-24","arxiv_id":"2201.09405","n_code_links":1,"syntology":null},{"paper":"/paper/patches-are-all-you-need-1","slug":"patches-are-all-you-need-1","title":"Patches Are All You Need?","date":"2022-01-24","arxiv_id":"2201.09792","n_code_links":12,"syntology":{"ran":8,"of":8,"n_ran_checked":7,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 5 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["locuslab/convmixer","tmp-iclr/convmixer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/transformers-in-medical-imaging-a-survey","slug":"transformers-in-medical-imaging-a-survey","title":"Transformers in Medical Imaging: A Survey","date":"2022-01-24","arxiv_id":"2201.09873","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-pre-trained-audio-visual-transformer-for","title":"A Pre-trained Audio-Visual Transformer for Emotion Recognition","date":"2022-01-23","arxiv_id":"2201.09165","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-for-deep-rgbt-tracking","title":"A Survey for Deep RGBT Tracking","date":"2022-01-23","arxiv_id":"2201.09296","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-expressiveness-of-transformer","slug":"investigating-expressiveness-of-transformer","title":"How Expressive are Transformers in Spectral Domain for Graphs?","date":"2022-01-23","arxiv_id":"2201.09332","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["ansonb/FeTA_TMLR"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/reconformer-accelerated-mri-reconstruction","slug":"reconformer-accelerated-mri-reconstruction","title":"ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer","date":"2022-01-23","arxiv_id":"2201.09376","n_code_links":1,"syntology":null},{"paper":null,"slug":"dual-flattening-transformers-through","title":"Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation","date":"2022-01-22","arxiv_id":"2201.09139","n_code_links":0,"syntology":null},{"paper":"/paper/representing-long-range-context-for-graph-1","slug":"representing-long-range-context-for-graph-1","title":"Representing Long-Range Context for Graph Neural Networks with Global Attention","date":"2022-01-21","arxiv_id":"2201.08821","n_code_links":1,"syntology":{"ran":7,"of":14,"n_ran_checked":6,"n_instrument":1,"unverified":7,"pointer_only":0,"phrase":"7 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; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["ucbrise/graphtrans"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/memvit-memory-augmented-multiscale-vision","slug":"memvit-memory-augmented-multiscale-vision","title":"MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition","date":"2022-01-20","arxiv_id":"2201.08383","n_code_links":1,"syntology":null},{"paper":null,"slug":"meta-learning-for-code-summarization-1","title":"Meta Learning for Code Summarization","date":"2022-01-20","arxiv_id":"2201.08310","n_code_links":0,"syntology":null},{"paper":"/paper/poseur-direct-human-pose-regression-with","slug":"poseur-direct-human-pose-regression-with","title":"Poseur: Direct Human Pose Regression with Transformers","date":"2022-01-19","arxiv_id":"2201.07412","n_code_links":1,"syntology":null},{"paper":"/paper/q-vit-fully-differentiable-quantization-for","slug":"q-vit-fully-differentiable-quantization-for","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","date":"2022-01-19","arxiv_id":"2201.07703","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zhexinli/Q-ViT-DeiT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"swin-pose-swin-transformer-based-human-pose","title":"Swin-Pose: Swin Transformer Based Human Pose Estimation","date":"2022-01-19","arxiv_id":"2201.07384","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfuse-a-unified-transformer-based-image","title":"TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning","date":"2022-01-19","arxiv_id":"2201.07451","n_code_links":0,"syntology":null},{"paper":null,"slug":"gtrans-spatiotemporal-autoregressive","title":"GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events","date":"2022-01-18","arxiv_id":"2201.06717","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-neural-network-approaches-for","title":"Hierarchical Neural Network Approaches for Long Document Classification","date":"2022-01-18","arxiv_id":"2201.06774","n_code_links":0,"syntology":null},{"paper":"/paper/motion-inbetweening-via-deep-d-interpolator","slug":"motion-inbetweening-via-deep-d-interpolator","title":"Motion Inbetweening via Deep $Δ$-Interpolator","date":"2022-01-18","arxiv_id":"2201.06701","n_code_links":1,"syntology":null},{"paper":"/paper/resistance-training-using-prior-bias-toward","slug":"resistance-training-using-prior-bias-toward","title":"Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation","date":"2022-01-18","arxiv_id":"2201.06794","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chch1999/rtpb"],"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"]}}},{"paper":null,"slug":"towards-controllable-protein-design-with","title":"Controllable Protein Design with Language Models","date":"2022-01-18","arxiv_id":"2201.07338","n_code_links":0,"syntology":null},{"paper":"/paper/continual-transformers-redundancy-free","slug":"continual-transformers-redundancy-free","title":"Continual Transformers: Redundancy-Free Attention for Online Inference","date":"2022-01-17","arxiv_id":"2201.06268","n_code_links":1,"syntology":null},{"paper":null,"slug":"disentangled-latent-transformer-for","title":"Disentangled Latent Transformer for Interpretable Monocular Height Estimation","date":"2022-01-17","arxiv_id":"2201.06357","n_code_links":0,"syntology":null},{"paper":"/paper/korean-specific-dataset-for-table-question","slug":"korean-specific-dataset-for-table-question","title":"Korean-Specific Dataset for Table Question Answering","date":"2022-01-17","arxiv_id":"2201.06223","n_code_links":1,"syntology":null},{"paper":null,"slug":"looking-at-the-performer-from-a-hopfield","title":"Looking at the Performer from a Hopfield Point of View","date":"2022-01-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/squire-a-sequence-to-sequence-framework-for","slug":"squire-a-sequence-to-sequence-framework-for","title":"SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning","date":"2022-01-17","arxiv_id":"2201.06206","n_code_links":1,"syntology":null},{"paper":null,"slug":"conventional-clustering-based-method-for","title":"Conventional clustering-based method for event detection on social networks","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ernie-layout-layout-knowledge-enhanced-multi","slug":"ernie-layout-layout-knowledge-enhanced-multi","title":"ERNIE-Layout: Layout-Knowledge Enhanced Multi-modal Pre-training for Document Understanding","date":"2022-01-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hierarchical-transformers-are-more-efficient-1","title":"Hierarchical Transformers Are More Efficient Language Models","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kat-a-knowledge-augmented-transformer-for-1","title":"KAT: A Knowledge Augmented Transformer for Vision-and-Language","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kd-vlp-improving-end-to-end-vision-and-1","title":"KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"lope-learnable-sinusoidal-positional-encoding","title":"LoPE: Learnable Sinusoidal Positional Encoding for Improving Document Transformer Model","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"must-a-framework-for-training-task-oriented","title":"MUST: A Framework for Training Task-oriented Dialogue Systems with Multiple User SimulaTors","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"non-autoregressive-neural-machine-translation-2","title":"Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"patching-leaks-in-the-charformer-for","title":"Patching Leaks in the Charformer for Generative Tasks","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"polling-latent-opinions-a-method-for","title":"Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"s5-framework-a-review-of-self-supervised","title":"S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"surprisingly-simple-adapter-ensembling-for","title":"Surprisingly Simple Adapter Ensembling for Zero-Shot Cross-Lingual Sequence Tagging","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tree-knowledge-distillation-for-compressing","title":"Tree Knowledge Distillation for Compressing Transformer-Based Language Models","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"video-transformers-a-survey","title":"Video Transformers: A Survey","date":"2022-01-16","arxiv_id":"2201.05991","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-adaptation-via-bidirectional-cross","title":"Domain Adaptation via Bidirectional Cross-Attention Transformer","date":"2022-01-15","arxiv_id":"2201.05887","n_code_links":0,"syntology":null},{"paper":"/paper/kformer-knowledge-injection-in-transformer","slug":"kformer-knowledge-injection-in-transformer","title":"Kformer: Knowledge Injection in Transformer Feed-Forward Layers","date":"2022-01-15","arxiv_id":"2201.05742","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["zjunlp/Kformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"vitbis-vision-transformer-for-biomedical","title":"ViTBIS: Vision Transformer for Biomedical Image Segmentation","date":"2022-01-15","arxiv_id":"2201.05920","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-over-self-attention-intention-aware","title":"Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation","date":"2022-01-14","arxiv_id":"2201.05333","n_code_links":0,"syntology":null},{"paper":"/paper/accurate-identification-of-bacteriophages","slug":"accurate-identification-of-bacteriophages","title":"Accurate identification of bacteriophages from metagenomic data using Transformer","date":"2022-01-13","arxiv_id":"2201.04778","n_code_links":1,"syntology":null},{"paper":null,"slug":"hand-object-interaction-reasoning","title":"Hand-Object Interaction Reasoning","date":"2022-01-13","arxiv_id":"2201.04906","n_code_links":0,"syntology":null},{"paper":null,"slug":"technical-report-for-iccv-2021-challenge","title":"Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners","date":"2022-01-13","arxiv_id":"2201.04924","n_code_links":0,"syntology":null},{"paper":"/paper/transvod-end-to-end-video-object-detection","slug":"transvod-end-to-end-video-object-detection","title":"TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers","date":"2022-01-13","arxiv_id":"2201.05047","n_code_links":3,"syntology":{"ran":6,"of":6,"n_ran_checked":3,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"6 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["qianyuzqy/TransVOD_Lite"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["named_in_paper","official"]}}},{"paper":null,"slug":"chaos-and-order-in-event-triggered-control","title":"Chaos and order in event-triggered control","date":"2022-01-12","arxiv_id":"2201.04462","n_code_links":0,"syntology":null},{"paper":"/paper/hypertransformer-model-generation-for","slug":"hypertransformer-model-generation-for","title":"HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning","date":"2022-01-11","arxiv_id":"2201.04182","n_code_links":2,"syntology":null},{"paper":null,"slug":"model-less-robust-voltage-control-in-active","title":"Model-less Robust Voltage Control in Active Distribution Networks using Sensitivity Coefficients Estimated from Measurements","date":"2022-01-11","arxiv_id":"2201.04192","n_code_links":0,"syntology":null},{"paper":null,"slug":"pyramid-fusion-transformer-for-semantic","title":"Pyramid Fusion Transformer for Semantic Segmentation","date":"2022-01-11","arxiv_id":"2201.04019","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-ginco-training-dataset-for-web-genre","title":"The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild","date":"2022-01-11","arxiv_id":"2201.03857","n_code_links":0,"syntology":null},{"paper":null,"slug":"uni-eden-universal-encoder-decoder-network-by","title":"Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training","date":"2022-01-11","arxiv_id":"2201.04026","n_code_links":0,"syntology":null},{"paper":"/paper/local-information-assisted-attention-free","slug":"local-information-assisted-attention-free","title":"Local Information Assisted Attention-free Decoder for Audio Captioning","date":"2022-01-10","arxiv_id":"2201.03217","n_code_links":1,"syntology":null},{"paper":null,"slug":"swin-transformers-make-strong-contextual","title":"Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction","date":"2022-01-10","arxiv_id":"2201.03178","n_code_links":0,"syntology":null},{"paper":null,"slug":"tiltedbert-resource-adjustable-version-of","title":"Latency Adjustable Transformer Encoder for Language Understanding","date":"2022-01-10","arxiv_id":"2201.03327","n_code_links":0,"syntology":null},{"paper":"/paper/spatio-temporal-tuples-transformer-for","slug":"spatio-temporal-tuples-transformer-for","title":"Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition","date":"2022-01-08","arxiv_id":"2201.02849","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":3,"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) · 4 unverified","official":{"repos":["heleiqiu/sttformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/automatic-speech-recognition-datasets-in","slug":"automatic-speech-recognition-datasets-in","title":"Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset","date":"2022-01-07","arxiv_id":"2201.02419","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-target-aware-representation-for","title":"Learning Target-aware Representation for Visual Tracking via Informative Interactions","date":"2022-01-07","arxiv_id":"2201.02526","n_code_links":0,"syntology":null},{"paper":"/paper/compact-bidirectional-transformer-for-image","slug":"compact-bidirectional-transformer-for-image","title":"Compact Bidirectional Transformer for Image Captioning","date":"2022-01-06","arxiv_id":"2201.01984","n_code_links":1,"syntology":null},{"paper":"/paper/flow-guided-sparse-transformer-for-video","slug":"flow-guided-sparse-transformer-for-video","title":"Flow-Guided Sparse Transformer for Video Deblurring","date":"2022-01-06","arxiv_id":"2201.01893","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["linjing7/VR-Baseline"],"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"]}}},{"paper":null,"slug":"transvpr-transformer-based-place-recognition","title":"TransVPR: Transformer-based place recognition with multi-level attention aggregation","date":"2022-01-06","arxiv_id":"2201.02001","n_code_links":0,"syntology":null},{"paper":"/paper/lawin-transformer-improving-semantic","slug":"lawin-transformer-improving-semantic","title":"Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention","date":"2022-01-05","arxiv_id":"2201.01615","n_code_links":3,"syntology":null},{"paper":null,"slug":"efficient-dyn-dynamic-graph-representation","title":"Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network","date":"2022-01-04","arxiv_id":"2201.01384","n_code_links":0,"syntology":null},{"paper":"/paper/pyramidtnt-improved-transformer-in","slug":"pyramidtnt-improved-transformer-in","title":"PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture","date":"2022-01-04","arxiv_id":"2201.00978","n_code_links":1,"syntology":null},{"paper":"/paper/sign-pose-based-transformer-for-word-level","slug":"sign-pose-based-transformer-for-word-level","title":"Sign Pose-Based Transformer for Word-Level Sign Language Recognition","date":"2022-01-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"caft-clustering-and-filter-on-tokens-of","title":"CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization","date":"2022-01-03","arxiv_id":"2201.00475","n_code_links":0,"syntology":null},{"paper":"/paper/d-former-a-u-shaped-dilated-transformer-for","slug":"d-former-a-u-shaped-dilated-transformer-for","title":"D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation","date":"2022-01-03","arxiv_id":"2201.00462","n_code_links":1,"syntology":null},{"paper":"/paper/language-as-queries-for-referring-video","slug":"language-as-queries-for-referring-video","title":"Language as Queries for Referring Video Object Segmentation","date":"2022-01-03","arxiv_id":"2201.00487","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wjn922/referformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/vision-transformer-with-deformable-attention","slug":"vision-transformer-with-deformable-attention","title":"Vision Transformer with Deformable Attention","date":"2022-01-03","arxiv_id":"2201.00520","n_code_links":2,"syntology":{"ran":8,"of":10,"n_ran_checked":4,"n_instrument":4,"unverified":2,"pointer_only":0,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["leaplabthu/dat"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/detail-preserving-transformer-for-light-field","slug":"detail-preserving-transformer-for-light-field","title":"Detail-Preserving Transformer for Light Field Image Super-Resolution","date":"2022-01-02","arxiv_id":"2201.00346","n_code_links":1,"syntology":{"ran":10,"of":21,"n_ran_checked":6,"n_instrument":4,"unverified":11,"pointer_only":21,"phrase":"10 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; 4 where Syntology's instrument failed) · 11 unverified","official":{"repos":["bitszwang/dpt"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":11,"ran_from_kinds":["official"]}}},{"paper":"/paper/informed-multi-context-entity-alignment","slug":"informed-multi-context-entity-alignment","title":"Informed Multi-context Entity Alignment","date":"2022-01-02","arxiv_id":"2201.00304","n_code_links":1,"syntology":null},{"paper":"/paper/splicing-vit-features-for-semantic-appearance","slug":"splicing-vit-features-for-semantic-appearance","title":"Splicing ViT Features for Semantic Appearance Transfer","date":"2022-01-02","arxiv_id":"2201.00424","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["omerbt/Splice"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-brand-new-dance-partner-music-conditioned","slug":"a-brand-new-dance-partner-music-conditioned","title":"A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres","date":"2022-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-graph-matching-perspective-with","title":"A Graph Matching Perspective With Transformers on Video Instance Segmentation","date":"2022-01-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"769f307899af7e416027cdaab7cf07e219fc7e36ab281f120cebfbdfd04735d4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}