{"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/retrieval/papers/41","list_of":"/task/retrieval","task":"Retrieval","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":41,"pages_in_order":143,"rows_per_page":100,"rows":[4001,4100],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"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/retrieval","prev":"/task/retrieval/papers/40","next":"/task/retrieval/papers/42","papers":[{"url":"/paper/learning-implicit-user-profiles-for","slug":"learning-implicit-user-profiles-for","title":"Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot","date":"2021-08-18","arxiv_id":"2108.07935","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-backbone-for-hyperspectral-image","slug":"a-new-backbone-for-hyperspectral-image","title":"A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging","date":"2021-08-17","arxiv_id":"2108.07739","repositories_listed":1,"syntology":null},{"url":"/paper/response-ranking-with-multi-types-of-deep","slug":"response-ranking-with-multi-types-of-deep","title":"Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues","date":"2021-08-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/complex-knowledge-base-question-answering-a","slug":"complex-knowledge-base-question-answering-a","title":"Complex Knowledge Base Question Answering: A Survey","date":"2021-08-15","arxiv_id":"2108.06688","repositories_listed":1,"syntology":null},{"url":"/paper/on-single-and-multiple-representations-in","slug":"on-single-and-multiple-representations-in","title":"On Single and Multiple Representations in Dense Passage Retrieval","date":"2021-08-13","arxiv_id":"2108.06279","repositories_listed":1,"syntology":null},{"url":"/paper/pair-leveraging-passage-centric-similarity","slug":"pair-leveraging-passage-centric-similarity","title":"PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval","date":"2021-08-13","arxiv_id":"2108.06027","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-corpus-aware-language-model-pre","slug":"unsupervised-corpus-aware-language-model-pre","title":"Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval","date":"2021-08-12","arxiv_id":"2108.05540","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-interaction-machine-for-tabular","slug":"retrieval-interaction-machine-for-tabular","title":"Retrieval & Interaction Machine for Tabular Data Prediction","date":"2021-08-11","arxiv_id":"2108.05252","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-user-behavior-retrieval-in-click","slug":"end-to-end-user-behavior-retrieval-in-click","title":"End-to-End User Behavior Retrieval in Click-Through RatePrediction Model","date":"2021-08-10","arxiv_id":"2108.04468","repositories_listed":1,"syntology":null},{"url":"/paper/dossier-coliee-2021-leveraging-dense","slug":"dossier-coliee-2021-leveraging-dense","title":"DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval","date":"2021-08-09","arxiv_id":"2108.03937","repositories_listed":1,"syntology":null},{"url":"/paper/skeleton-contrastive-3d-action-representation","slug":"skeleton-contrastive-3d-action-representation","title":"Skeleton-Contrastive 3D Action Representation Learning","date":"2021-08-08","arxiv_id":"2108.03656","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/skeleton-contrastive-3d-action-representation#ran","syntology_url":"https://syntology.ai/paper/2108.03656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03656"}},"official":{"repos":["fmthoker/skeleton-contrast"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/adaptive-label-aware-graph-convolutional","slug":"adaptive-label-aware-graph-convolutional","title":"Adaptive label-aware graph convolutional networks for cross-modal retrieval","date":"2021-08-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dual-tuning-joint-prototype-transfer-and","slug":"dual-tuning-joint-prototype-transfer-and","title":"Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning","date":"2021-08-06","arxiv_id":"2108.02959","repositories_listed":1,"syntology":null},{"url":"/paper/video-contrastive-learning-with-global","slug":"video-contrastive-learning-with-global","title":"Video Contrastive Learning with Global Context","date":"2021-08-05","arxiv_id":"2108.02722","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/video-contrastive-learning-with-global#ran","syntology_url":"https://syntology.ai/paper/2108.02722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02722"}},"official":{"repos":["amazon-research/video-contrastive-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/cnn-based-autoencoder-application-in-breast","slug":"cnn-based-autoencoder-application-in-breast","title":"CNN Based Autoencoder Application in Breast Cancer Image Retrieval","date":"2021-08-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/icecap-information-concentrated-entity-aware","slug":"icecap-information-concentrated-entity-aware","title":"ICECAP: Information Concentrated Entity-aware Image Captioning","date":"2021-08-04","arxiv_id":"2108.02050","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compatible-embeddings","slug":"learning-compatible-embeddings","title":"Learning Compatible Embeddings","date":"2021-08-04","arxiv_id":"2108.01958","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-compatible-embeddings#ran","syntology_url":"https://syntology.ai/paper/2108.01958","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01958"}},"official":{"repos":["IrvingMeng/LCE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/predicting-music-relistening-behavior-using","slug":"predicting-music-relistening-behavior-using","title":"Predicting Music Relistening Behavior Using the ACT-R Framework","date":"2021-08-04","arxiv_id":"2108.02138","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-pose-estimation-of","slug":"transfer-learning-for-pose-estimation-of","title":"Transfer Learning for Pose Estimation of Illustrated Characters","date":"2021-08-04","arxiv_id":"2108.01819","repositories_listed":1,"syntology":null},{"url":"/paper/iart-a-search-engine-for-art-historical","slug":"iart-a-search-engine-for-art-historical","title":"iART: A Search Engine for Art-Historical Images to Support Research in the Humanities","date":"2021-08-03","arxiv_id":"2108.01542","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-human-reading-comprehension","slug":"understanding-human-reading-comprehension","title":"Towards a Better Understanding Human Reading Comprehension with Brain Signals","date":"2021-08-03","arxiv_id":"2108.01360","repositories_listed":1,"syntology":null},{"url":"/paper/convert-an-application-to-faq-answering","slug":"convert-an-application-to-faq-answering","title":"ConveRT for FAQ Answering","date":"2021-08-02","arxiv_id":"2108.00719","repositories_listed":1,"syntology":null},{"url":"/paper/learning-tfidf-enhanced-joint-embedding-for","slug":"learning-tfidf-enhanced-joint-embedding-for","title":"Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service","date":"2021-08-02","arxiv_id":"2108.00724","repositories_listed":1,"syntology":null},{"url":"/paper/a-dqn-based-approach-to-finding-precise","slug":"a-dqn-based-approach-to-finding-precise","title":"A DQN-based Approach to Finding Precise Evidences for Fact Verification","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-gradually-soft-multi-task-and-data","slug":"a-gradually-soft-multi-task-and-data","title":"A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-facet-selection-by-maximizing-graded","slug":"dynamic-facet-selection-by-maximizing-graded","title":"Dynamic Facet Selection by Maximizing Graded Relevance","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/space-efficient-context-encoding-for-non-task","slug":"space-efficient-context-encoding-for-non-task","title":"Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mtvr-multilingual-moment-retrieval-in-videos","slug":"mtvr-multilingual-moment-retrieval-in-videos","title":"MTVR: Multilingual Moment Retrieval in Videos","date":"2021-07-30","arxiv_id":"2108.00061","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":4,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mtvr-multilingual-moment-retrieval-in-videos#ran","syntology_url":"https://syntology.ai/paper/2108.00061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00061"}},"official":{"repos":["jayleicn/mTVRetrieval"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/product1m-towards-weakly-supervised-instance","slug":"product1m-towards-weakly-supervised-instance","title":"Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining","date":"2021-07-30","arxiv_id":"2107.14572","repositories_listed":1,"syntology":null},{"url":"/paper/domain-matched-pre-training-tasks-for-dense","slug":"domain-matched-pre-training-tasks-for-dense","title":"Domain-matched Pre-training Tasks for Dense Retrieval","date":"2021-07-28","arxiv_id":"2107.13602","repositories_listed":1,"syntology":null},{"url":"/paper/goal-oriented-script-construction","slug":"goal-oriented-script-construction","title":"Goal-Oriented Script Construction","date":"2021-07-28","arxiv_id":"2107.13189","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-implicit-shape-and-pose-learning","slug":"disentangled-implicit-shape-and-pose-learning","title":"DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation","date":"2021-07-27","arxiv_id":"2107.12549","repositories_listed":1,"syntology":null},{"url":"/paper/enriching-local-and-global-contexts-for","slug":"enriching-local-and-global-contexts-for","title":"Enriching Local and Global Contexts for Temporal Action Localization","date":"2021-07-27","arxiv_id":"2107.12960","repositories_listed":1,"syntology":null},{"url":"/paper/red-dragon-ai-at-textgraphs-2021-shared-task","slug":"red-dragon-ai-at-textgraphs-2021-shared-task","title":"Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings","date":"2021-07-27","arxiv_id":"2107.13031","repositories_listed":1,"syntology":null},{"url":"/paper/hanet-hierarchical-alignment-networks-for","slug":"hanet-hierarchical-alignment-networks-for","title":"HANet: Hierarchical Alignment Networks for Video-Text Retrieval","date":"2021-07-26","arxiv_id":"2107.12059","repositories_listed":1,"syntology":null},{"url":"/paper/one-question-answering-model-for-many","slug":"one-question-answering-model-for-many","title":"One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval","date":"2021-07-26","arxiv_id":"2107.11976","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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/one-question-answering-model-for-many#ran","syntology_url":"https://syntology.ai/paper/2107.11976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11976"}},"official":{"repos":["AkariAsai/CORA"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-stage-pre-training-over-simplified","slug":"multi-stage-pre-training-over-simplified","title":"Multi-stage Pre-training over Simplified Multimodal Pre-training Models","date":"2021-07-22","arxiv_id":"2107.14596","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":9,"phrase":"3 ran (of which 3 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) · 6 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-stage-pre-training-over-simplified#ran","syntology_url":"https://syntology.ai/paper/2107.14596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.14596"}},"official":{"repos":["lttsmn/LXMERT-S"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/linked-data-triples-enhance-document","slug":"linked-data-triples-enhance-document","title":"Linked Data Triples Enhance Document Relevance Classification","date":"2021-07-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/wikigraphs-a-wikipedia-text-knowledge-graph","slug":"wikigraphs-a-wikipedia-text-knowledge-graph","title":"WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset","date":"2021-07-20","arxiv_id":"2107.09556","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-multi-modal-dialogue-dataset-by","slug":"constructing-multi-modal-dialogue-dataset-by","title":"Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images","date":"2021-07-19","arxiv_id":"2107.08685","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/constructing-multi-modal-dialogue-dataset-by#ran","syntology_url":"https://syntology.ai/paper/2107.08685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08685"}},"official":{"repos":["shh1574/multi-modal-dialogue-dataset"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-identification-of-relevant-prior","slug":"unsupervised-identification-of-relevant-prior","title":"Unsupervised Identification of Relevant Prior Cases","date":"2021-07-19","arxiv_id":"2107.08973","repositories_listed":1,"syntology":null},{"url":"/paper/proactive-retrieval-based-chatbots-based-on","slug":"proactive-retrieval-based-chatbots-based-on","title":"Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals","date":"2021-07-18","arxiv_id":"2107.08329","repositories_listed":1,"syntology":null},{"url":"/paper/more-robust-dense-retrieval-with-contrastive","slug":"more-robust-dense-retrieval-with-contrastive","title":"More Robust Dense Retrieval with Contrastive Dual Learning","date":"2021-07-16","arxiv_id":"2107.07773","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/more-robust-dense-retrieval-with-contrastive#ran","syntology_url":"https://syntology.ai/paper/2107.07773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07773"}},"official":{"repos":["thunlp/DANCE"],"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/cadsketchnet-an-annotated-sketch-dataset-for","slug":"cadsketchnet-an-annotated-sketch-dataset-for","title":"'CADSketchNet' -- An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks","date":"2021-07-13","arxiv_id":"2107.06212","repositories_listed":1,"syntology":null},{"url":"/paper/hat-hierarchical-aggregation-transformers-for","slug":"hat-hierarchical-aggregation-transformers-for","title":"HAT: Hierarchical Aggregation Transformers for Person Re-identification","date":"2021-07-13","arxiv_id":"2107.05946","repositories_listed":1,"syntology":null},{"url":"/paper/retrieve-in-style-unsupervised-facial-feature","slug":"retrieve-in-style-unsupervised-facial-feature","title":"Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval","date":"2021-07-13","arxiv_id":"2107.06256","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/retrieve-in-style-unsupervised-facial-feature#ran","syntology_url":"https://syntology.ai/paper/2107.06256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.06256"}},"official":{"repos":["mchong6/RetrieveInStyle"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/codified-audio-language-modeling-learns","slug":"codified-audio-language-modeling-learns","title":"Codified audio language modeling learns useful representations for music information retrieval","date":"2021-07-12","arxiv_id":"2107.05677","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/codified-audio-language-modeling-learns#ran","syntology_url":"https://syntology.ai/paper/2107.05677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05677"}},"official":{"repos":["p-lambda/jukemir"],"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"]}}},{"url":"/paper/on-the-evaluation-of-commit-message","slug":"on-the-evaluation-of-commit-message","title":"On the Evaluation of Commit Message Generation Models: An Experimental Study","date":"2021-07-12","arxiv_id":"2107.05373","repositories_listed":1,"syntology":null},{"url":"/paper/splade-sparse-lexical-and-expansion-model-for","slug":"splade-sparse-lexical-and-expansion-model-for","title":"SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking","date":"2021-07-12","arxiv_id":"2107.05720","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-modality-interaction-modeling-for","slug":"dynamic-modality-interaction-modeling-for","title":"Dynamic Modality Interaction Modeling for Image-Text Retrieval","date":"2021-07-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/metap-meta-pattern-learning-for-one-shot","slug":"metap-meta-pattern-learning-for-one-shot","title":"MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion","date":"2021-07-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/partial-3d-object-retrieval-using-local","slug":"partial-3d-object-retrieval-using-local","title":"Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing","date":"2021-07-07","arxiv_id":"2107.03368","repositories_listed":1,"syntology":null},{"url":"/paper/vidlankd-improving-language-understanding-via","slug":"vidlankd-improving-language-understanding-via","title":"VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer","date":"2021-07-06","arxiv_id":"2107.02681","repositories_listed":1,"syntology":null},{"url":"/paper/dppin-a-biological-dataset-of-dynamic-protein","slug":"dppin-a-biological-dataset-of-dynamic-protein","title":"DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data","date":"2021-07-05","arxiv_id":"2107.02168","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-audio-visual-alignments-in","slug":"evaluation-of-audio-visual-alignments-in","title":"Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models","date":"2021-07-05","arxiv_id":"2108.02562","repositories_listed":1,"syntology":null},{"url":"/paper/graph-convolution-for-re-ranking-in-person-re","slug":"graph-convolution-for-re-ranking-in-person-re","title":"Graph Convolution for Re-ranking in Person Re-identification","date":"2021-07-05","arxiv_id":"2107.02220","repositories_listed":1,"syntology":null},{"url":"/paper/part2word-learning-joint-embedding-of-point","slug":"part2word-learning-joint-embedding-of-point","title":"Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words","date":"2021-07-05","arxiv_id":"2107.01872","repositories_listed":1,"syntology":{"n":10,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/part2word-learning-joint-embedding-of-point#ran","syntology_url":"https://syntology.ai/paper/2107.01872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01872"}},"official":{"repos":["jlutangchuan/parts2words"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/tagrec-automated-tagging-of-questions-with","slug":"tagrec-automated-tagging-of-questions-with","title":"TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy","date":"2021-07-03","arxiv_id":"2107.10649","repositories_listed":1,"syntology":null},{"url":"/paper/how-incomplete-is-contrastive-learning","slug":"how-incomplete-is-contrastive-learning","title":"Inter-intra Variant Dual Representations forSelf-supervised Video Recognition","date":"2021-07-02","arxiv_id":"2107.01194","repositories_listed":1,"syntology":null},{"url":"/paper/fedcmr-federated-cross-modal-retrieval","slug":"fedcmr-federated-cross-modal-retrieval","title":"FedCMR: Federated Cross-Modal Retrieval","date":"2021-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/orthonormal-product-quantization-network-for","slug":"orthonormal-product-quantization-network-for","title":"Orthonormal Product Quantization Network for Scalable Face Image Retrieval","date":"2021-07-01","arxiv_id":"2107.00327","repositories_listed":1,"syntology":null},{"url":"/paper/news-article-retrieval-in-context-for-event","slug":"news-article-retrieval-in-context-for-event","title":"News Article Retrieval in Context for Event-centric Narrative Creation","date":"2021-06-30","arxiv_id":"2106.16053","repositories_listed":1,"syntology":null},{"url":"/paper/towards-sample-optimal-compressive-phase","slug":"towards-sample-optimal-compressive-phase","title":"Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors","date":"2021-06-29","arxiv_id":"2106.15358","repositories_listed":1,"syntology":null},{"url":"/paper/keyphrase-generation-for-scientific-document-1","slug":"keyphrase-generation-for-scientific-document-1","title":"Keyphrase Generation for Scientific Document Retrieval","date":"2021-06-28","arxiv_id":"2106.14726","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-research-trends-in-inorganic","slug":"analyzing-research-trends-in-inorganic","title":"Analyzing Research Trends in Inorganic Materials Literature Using NLP","date":"2021-06-27","arxiv_id":"2106.14157","repositories_listed":1,"syntology":null},{"url":"/paper/a-modern-perspective-on-query-likelihood-with","slug":"a-modern-perspective-on-query-likelihood-with","title":"A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models","date":"2021-06-25","arxiv_id":"2106.13618","repositories_listed":1,"syntology":null},{"url":"/paper/dns-distill-and-select-for-efficient-and","slug":"dns-distill-and-select-for-efficient-and","title":"DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval","date":"2021-06-24","arxiv_id":"2106.13266","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/dns-distill-and-select-for-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2106.13266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.13266"}},"official":{"repos":["mever-team/distill-and-select"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/learnt-sparsity-for-effective-and","slug":"learnt-sparsity-for-effective-and","title":"Extractive Explanations for Interpretable Text Ranking","date":"2021-06-23","arxiv_id":"2106.12460","repositories_listed":1,"syntology":null},{"url":"/paper/domain-smoothing-network-for-zero-shot-sketch","slug":"domain-smoothing-network-for-zero-shot-sketch","title":"Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval","date":"2021-06-22","arxiv_id":"2106.11841","repositories_listed":1,"syntology":null},{"url":"/paper/exemplars-guided-empathetic-response","slug":"exemplars-guided-empathetic-response","title":"Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication","date":"2021-06-22","arxiv_id":"2106.11791","repositories_listed":1,"syntology":null},{"url":"/paper/information-retrieval-for-zerospeech-2021-the","slug":"information-retrieval-for-zerospeech-2021-the","title":"Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw","date":"2021-06-22","arxiv_id":"2106.11603","repositories_listed":1,"syntology":null},{"url":"/paper/clip2video-mastering-video-text-retrieval-via","slug":"clip2video-mastering-video-text-retrieval-via","title":"CLIP2Video: Mastering Video-Text Retrieval via Image CLIP","date":"2021-06-21","arxiv_id":"2106.11097","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/clip2video-mastering-video-text-retrieval-via#ran","syntology_url":"https://syntology.ai/paper/2106.11097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11097"}},"official":{"repos":["CryhanFang/CLIP2Video"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/contrastive-learning-for-natural-language","slug":"contrastive-learning-for-natural-language","title":"Contrastive learning for natural language-based vehicle retrieval","date":"2021-06-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cosmo-content-style-modulation-for-image","slug":"cosmo-content-style-modulation-for-image","title":"CoSMo: Content-Style Modulation for Image Retrieval With Text Feedback","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-cross-modal-retrieval-with-noisy","slug":"learning-cross-modal-retrieval-with-noisy","title":"Learning Cross-Modal Retrieval With Noisy Labels","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-video-hashing-via","slug":"self-supervised-video-hashing-via","title":"Self-Supervised Video Hashing via Bidirectional Transformers","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/all-you-can-embed-natural-language-based","slug":"all-you-can-embed-natural-language-based","title":"All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers","date":"2021-06-18","arxiv_id":"2106.10153","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-video-representation-learning-7","slug":"self-supervised-video-representation-learning-7","title":"Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting","date":"2021-06-18","arxiv_id":"2106.10137","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-pre-training-for-multi-hop","slug":"weakly-supervised-pre-training-for-multi-hop","title":"Weakly Supervised Pre-Training for Multi-Hop Retriever","date":"2021-06-18","arxiv_id":"2106.09983","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/weakly-supervised-pre-training-for-multi-hop#ran","syntology_url":"https://syntology.ai/paper/2106.09983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09983"}},"official":{"repos":["yeonsw/LOUVRE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/an-information-retrieval-approach-to-building","slug":"an-information-retrieval-approach-to-building","title":"An Information Retrieval Approach to Building Datasets for Hate Speech Detection","date":"2021-06-17","arxiv_id":"2106.09775","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/an-information-retrieval-approach-to-building#ran","syntology_url":"https://syntology.ai/paper/2106.09775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09775"}},"official":{"repos":["mdmustafizurrahman/An-Information-Retrieval-Approach-to-Building-Datasets-for-Hate-Speech-Detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pen4rec-preference-evolution-networks-for","slug":"pen4rec-preference-evolution-networks-for","title":"PEN4Rec: Preference Evolution Networks for Session-based Recommendation","date":"2021-06-17","arxiv_id":"2106.09306","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-deep-learning-a-survey-on-making","slug":"efficient-deep-learning-a-survey-on-making","title":"Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better","date":"2021-06-16","arxiv_id":"2106.08962","repositories_listed":1,"syntology":null},{"url":"/paper/probing-image-language-transformers-for-verb","slug":"probing-image-language-transformers-for-verb","title":"Probing Image-Language Transformers for Verb Understanding","date":"2021-06-16","arxiv_id":"2106.09141","repositories_listed":1,"syntology":null},{"url":"/paper/analysing-dense-passage-retrieval-for-multi","slug":"analysing-dense-passage-retrieval-for-multi","title":"Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering","date":"2021-06-15","arxiv_id":"2106.08433","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-sketch-search","slug":"compositional-sketch-search","title":"Compositional Sketch Search","date":"2021-06-15","arxiv_id":"2106.08009","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-entity-disambiguation-and-the-role","slug":"evaluating-entity-disambiguation-and-the-role","title":"Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP","date":"2021-06-12","arxiv_id":"2106.06830","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-to-enhance-generalization-of-deep","slug":"a-framework-to-enhance-generalization-of-deep","title":"A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks","date":"2021-06-11","arxiv_id":"2106.06420","repositories_listed":1,"syntology":null},{"url":"/paper/augnet-end-to-end-unsupervised-visual","slug":"augnet-end-to-end-unsupervised-visual","title":"AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation","date":"2021-06-11","arxiv_id":"2106.06250","repositories_listed":1,"syntology":null},{"url":"/paper/refining-pseudo-labels-with-clustering","slug":"refining-pseudo-labels-with-clustering","title":"Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification","date":"2021-06-11","arxiv_id":"2106.06133","repositories_listed":1,"syntology":null},{"url":"/paper/augnlg-few-shot-natural-language-generation","slug":"augnlg-few-shot-natural-language-generation","title":"AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation","date":"2021-06-10","arxiv_id":"2106.05589","repositories_listed":1,"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/augnlg-few-shot-natural-language-generation#ran","syntology_url":"https://syntology.ai/paper/2106.05589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05589"}},"official":{"repos":["XinnuoXu/AugNLG"],"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/date-estimation-in-the-wild-of-scanned","slug":"date-estimation-in-the-wild-of-scanned","title":"Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach","date":"2021-06-10","arxiv_id":"2106.05618","repositories_listed":1,"syntology":null},{"url":"/paper/linguistically-informed-masking-for","slug":"linguistically-informed-masking-for","title":"Linguistically Informed Masking for Representation Learning in the Patent Domain","date":"2021-06-10","arxiv_id":"2106.05768","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-rank-words-optimizing-ranking","slug":"learning-to-rank-words-optimizing-ranking","title":"Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting","date":"2021-06-09","arxiv_id":"2106.05144","repositories_listed":1,"syntology":null},{"url":"/paper/time-frequency-phase-retrieval-for-audio-the","slug":"time-frequency-phase-retrieval-for-audio-the","title":"Time-Frequency Phase Retrieval for Audio -- The Effect of Transform Parameters","date":"2021-06-09","arxiv_id":"2106.05148","repositories_listed":1,"syntology":null},{"url":"/paper/cheap-and-good-simple-and-effective-data","slug":"cheap-and-good-simple-and-effective-data","title":"Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading","date":"2021-06-08","arxiv_id":"2106.04134","repositories_listed":1,"syntology":null},{"url":"/paper/cltr-an-end-to-end-transformer-based-system","slug":"cltr-an-end-to-end-transformer-based-system","title":"CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering","date":"2021-06-08","arxiv_id":"2106.04441","repositories_listed":1,"syntology":null},{"url":"/paper/conversational-fashion-image-retrieval-via","slug":"conversational-fashion-image-retrieval-via","title":"Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback","date":"2021-06-08","arxiv_id":"2106.04128","repositories_listed":1,"syntology":null},{"url":"/paper/sdgmnet-statistic-based-dynamic-gradient","slug":"sdgmnet-statistic-based-dynamic-gradient","title":"SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning","date":"2021-06-08","arxiv_id":"2106.04434","repositories_listed":1,"syntology":null},{"url":"/paper/value-a-multi-task-benchmark-for-video-and","slug":"value-a-multi-task-benchmark-for-video-and","title":"VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation","date":"2021-06-08","arxiv_id":"2106.04632","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/value-a-multi-task-benchmark-for-video-and#ran","syntology_url":"https://syntology.ai/paper/2106.04632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04632"}},"official":{"repos":["VALUE-Leaderboard/StarterCode"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-comprehensive-assessment-of-dialog","slug":"a-comprehensive-assessment-of-dialog","title":"A Comprehensive Assessment of Dialog Evaluation Metrics","date":"2021-06-07","arxiv_id":"2106.03706","repositories_listed":1,"syntology":null}],"record_sha256":"2c9de85057b4d5b716481008cf00bbf504bb061c904957793b5259cf61801c02","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}