{"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/image-captioning/papers/17","list_of":"/task/image-captioning","task":"Image Captioning","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":17,"pages_in_order":19,"rows_per_page":100,"rows":[1601,1700],"of":1878,"counts":{"archive_papers_tagged":1878,"with_a_code_link":774,"where_syntology_ran_a_sample":243,"not_listed_spam_title":0,"listed":1878,"listed_where_code_ran":243,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":201,"every_run_a_failure_of_syntologys_instrument":42,"listed_with_a_run_with_no_instrument_failure":201,"listed_every_run_a_failure_of_syntologys_instrument":42,"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/image-captioning","prev":"/task/image-captioning/papers/16","next":"/task/image-captioning/papers/18","papers":[{"url":null,"slug":"capsal-leveraging-captioning-to-boost","title":"CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"look-back-and-predict-forward-in-image","title":"Look Back and Predict Forward in Image Captioning","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mscap-multi-style-image-captioning-with","title":"MSCap: Multi-Style Image Captioning With Unpaired Stylized Text","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-to-language-tasks-based-on-attributes","title":"Vision-to-Language Tasks Based on Attributes and Attention Mechanism","date":"2019-05-29","arxiv_id":"1905.12243","repositories_listed":0,"syntology":null},{"url":null,"slug":"supercaptioning-image-captioning-using-two","title":"SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding","date":"2019-05-25","arxiv_id":"1905.10515","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-based-on-deep-learning","title":"Image Captioning based on Deep Learning Methods: A Survey","date":"2019-05-20","arxiv_id":"1905.08110","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-transformer-with-multi-view-visual","title":"Multimodal Transformer with Multi-View Visual Representation for Image Captioning","date":"2019-05-20","arxiv_id":"1905.07841","repositories_listed":0,"syntology":null},{"url":null,"slug":"harvesting-information-from-captions-for","title":"Harvesting Information from Captions for Weakly Supervised Semantic Segmentation","date":"2019-05-16","arxiv_id":"1905.06784","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-barycenter-model-ensembling","title":"Wasserstein Barycenter Model Ensembling","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowing-when-to-stop-evaluation-and","title":"Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications","date":"2019-04-26","arxiv_id":"1904.12004","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointing-novel-objects-in-image-captioning","title":"Pointing Novel Objects in Image Captioning","date":"2019-04-25","arxiv_id":"1904.11251","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-collocate-neural-modules-for","title":"Learning to Collocate Neural Modules for Image Captioning","date":"2019-04-18","arxiv_id":"1904.08608","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-critical-n-step-training-for-image","title":"Self-critical n-step Training for Image Captioning","date":"2019-04-15","arxiv_id":"1904.06861","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-adversarial-image-captioning","title":"Improved Adversarial Image Captioning","date":"2019-03-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-image-captioning-via-scene-graph","title":"Unpaired Image Captioning via Scene Graph Alignments","date":"2019-03-26","arxiv_id":"1903.10658","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosted-attention-leveraging-human-attention-1","title":"Boosted Attention: Leveraging Human Attention for Image Captioning","date":"2019-03-18","arxiv_id":"1904.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-weighted-multi-criteria-decision-making","title":"A Weighted Multi-Criteria Decision Making Approach for Image Captioning","date":"2019-03-17","arxiv_id":"1904.00766","repositories_listed":0,"syntology":null},{"url":"/paper/a-synchronized-multi-modal-attention-caption","slug":"a-synchronized-multi-modal-attention-caption","title":"Human Attention in Image Captioning: Dataset and Analysis","date":"2019-03-06","arxiv_id":"1903.02499","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-with-weakly-supervised","title":"Image captioning with weakly-supervised attention penalty","date":"2019-03-06","arxiv_id":"1903.02507","repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-a-hint-leveraging-explanations-to-make","title":"Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded","date":"2019-02-11","arxiv_id":"1902.03751","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-visual-relationships-for-high","title":"VrR-VG: Refocusing Visually-Relevant Relationships","date":"2019-02-01","arxiv_id":"1902.00313","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-image-captioning-by-leveraging","title":"Improving Image Captioning by Leveraging Knowledge Graphs","date":"2019-01-25","arxiv_id":"1901.08942","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-correcting-neural-sequence-prediction","title":"Error-Correcting Neural Sequence Prediction","date":"2019-01-21","arxiv_id":"1901.07002","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-image-selection-bison-interpretable","title":"Evaluating Text-to-Image Matching using Binary Image Selection (BISON)","date":"2019-01-19","arxiv_id":"1901.06595","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sequence-to-sequence-learning-via","title":"Improving Sequence-to-Sequence Learning via Optimal Transport","date":"2019-01-18","arxiv_id":"1901.06283","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-review-text-generation-with","title":"Image Based Review Text Generation with Emotional Guidance","date":"2019-01-14","arxiv_id":"1901.04140","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-lstms-with-adaptive-attention","title":"Hierarchical LSTMs with Adaptive Attention for Visual Captioning","date":"2018-12-26","arxiv_id":"1812.11004","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-fusion-effects-of-tensor-product","title":"Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images","date":"2018-12-17","arxiv_id":"1812.06624","repositories_listed":0,"syntology":null},{"url":null,"slug":"attend-more-times-for-image-captioning","title":"Attend More Times for Image Captioning","date":"2018-12-08","arxiv_id":"1812.03283","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attempt-towards-interpretable-audio-visual","title":"An Attempt towards Interpretable Audio-Visual Video Captioning","date":"2018-12-07","arxiv_id":"1812.02872","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-task-understanding-in-visual-settings","title":"Towards Task Understanding in Visual Settings","date":"2018-11-28","arxiv_id":"1811.11833","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-test-time-evidence-to-improve","title":"A Novel Technique for Evidence based Conditional Inference in Deep Neural Networks via Latent Feature Perturbation","date":"2018-11-24","arxiv_id":"1811.09796","repositories_listed":0,"syntology":null},{"url":null,"slug":"senti-attend-image-captioning-using-sentiment","title":"Senti-Attend: Image Captioning using Sentiment and Attention","date":"2018-11-24","arxiv_id":"1811.09789","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-model-for-scene-graph","title":"An Interpretable Model for Scene Graph Generation","date":"2018-11-21","arxiv_id":"1811.09543","repositories_listed":0,"syntology":null},{"url":null,"slug":"intention-oriented-image-captions-with","title":"Intention Oriented Image Captions with Guiding Objects","date":"2018-11-19","arxiv_id":"1811.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-based-on-a-hierarchical","title":"Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization","date":"2018-11-13","arxiv_id":"1811.05253","repositories_listed":0,"syntology":null},{"url":null,"slug":"atts2s-vc-sequence-to-sequence-voice","title":"AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms","date":"2018-11-09","arxiv_id":"1811.04076","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sequential-guiding-network-with-attention","title":"A sequential guiding network with attention for image captioning","date":"2018-11-01","arxiv_id":"1811.00228","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-strategies-for-neural-referring","title":"Decoding Strategies for Neural Referring Expression Generation","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-description-for-sequential-images","title":"Generating Description for Sequential Images with Local-Object Attention Conditioned on Global Semantic Context","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"importance-of-self-attention-for-sentiment","title":"Importance of Self-Attention for Sentiment Analysis","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-task-matters-comparing-image-captioning","title":"The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"treat-the-system-like-a-human-student","title":"Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"engaging-image-captioning-via-personality","title":"Engaging Image Captioning Via Personality","date":"2018-10-25","arxiv_id":"1810.10665","repositories_listed":0,"syntology":null},{"url":null,"slug":"bringing-back-simplicity-and-lightliness-into","title":"Bringing back simplicity and lightliness into neural image captioning","date":"2018-10-15","arxiv_id":"1810.06245","repositories_listed":0,"syntology":null},{"url":"/paper/look-deeper-see-richer-depth-aware-image","slug":"look-deeper-see-richer-depth-aware-image","title":"Look Deeper See Richer: Depth-aware Image Paragraph Captioning","date":"2018-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-to-video-person-re-identification-by","title":"Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings","date":"2018-10-04","arxiv_id":"1810.03989","repositories_listed":0,"syntology":null},{"url":null,"slug":"calcs-continuously-approximating-longest","title":"CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disambiguated-skip-gram-model","title":"Disambiguated skip-gram model","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emojigan-learning-emojis-distributions-with-a","title":"EmojiGAN: learning emojis distributions with a generative model","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grounding-semantic-roles-in-images","title":"Grounding Semantic Roles in Images","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"input-combination-strategies-for-multi-source","title":"Input Combination Strategies for Multi-Source Transformer Decoder","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-differential-network-for-visual-1","title":"Multimodal Differential Network for Visual Question Generation","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-expected-bleu-for-text","title":"Differentiable Expected BLEU for Text Generation","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graphseq2seq-graph-sequence-to-sequence-for","title":"GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantically-invariant-text-to-image","title":"Semantically Invariant Text-to-Image Generation","date":"2018-09-27","arxiv_id":"1809.10274","repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-learning-for-cross-domain","title":"Vector Learning for Cross Domain Representations","date":"2018-09-27","arxiv_id":"1809.10312","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-compositional-paradigm-for-image-1","title":"A Neural Compositional Paradigm for Image Captioning","date":"2018-09-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"textually-enriched-neural-module-networks-for","title":"Textually Enriched Neural Module Networks for Visual Question Answering","date":"2018-09-23","arxiv_id":"1809.08697","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-visual-relationship-for-image","title":"Exploring Visual Relationship for Image Captioning","date":"2018-09-19","arxiv_id":"1809.07041","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-accountable-ai-hybrid-human-machine","title":"Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure","date":"2018-09-19","arxiv_id":"1809.07424","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-based-on-deep-reinforcement","title":"Image Captioning based on Deep Reinforcement Learning","date":"2018-09-13","arxiv_id":"1809.04835","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-image-captioning-exploits-1","title":"End-to-end Image Captioning Exploits Multimodal Distributional Similarity","date":"2018-09-11","arxiv_id":"1809.04144","repositories_listed":0,"syntology":null},{"url":null,"slug":"spass-scientific-prominence-active-search","title":"SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network","date":"2018-09-10","arxiv_id":"1809.03385","repositories_listed":0,"syntology":null},{"url":"/paper/diverse-and-coherent-paragraph-generation","slug":"diverse-and-coherent-paragraph-generation","title":"Diverse and Coherent Paragraph Generation from Images","date":"2018-09-03","arxiv_id":"1809.00681","repositories_listed":0,"syntology":null},{"url":"/paper/chittron-an-automatic-bangla-image-captioning","slug":"chittron-an-automatic-bangla-image-captioning","title":"Chittron: An Automatic Bangla Image Captioning System","date":"2018-09-02","arxiv_id":"1809.00339","repositories_listed":0,"syntology":null},{"url":null,"slug":"factual-or-emotional-stylized-image-1","title":"``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nneval-neural-network-based-evaluation-metric","title":"NNEval: Neural Network based Evaluation Metric for Image Captioning","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"when-to-finish-optimal-beam-search-for-neural","title":"When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)","date":"2018-08-31","arxiv_id":"1809.00069","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-reference-training-with-pseudo","title":"Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation","date":"2018-08-28","arxiv_id":"1808.09564","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-distribution-matching-for","title":"Approximate Distribution Matching for Sequence-to-Sequence Learning","date":"2018-08-24","arxiv_id":"1808.08003","repositories_listed":0,"syntology":null},{"url":null,"slug":"dropout-during-inference-as-a-model-for","title":"Dropout during inference as a model for neurological degeneration in an image captioning network","date":"2018-08-11","arxiv_id":"1808.03747","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-feature-selection-with-attention-in","title":"Dynamic Feature Selection with Attention in Incremental Parsing","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-deep-visual-features-into","title":"Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-embeddings-for-unsupervised","title":"Using Word Embeddings for Unsupervised Acronym Disambiguation","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-dataset-for","title":"Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"doubly-attentive-transformer-machine","title":"Doubly Attentive Transformer Machine Translation","date":"2018-07-30","arxiv_id":"1807.11605","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-fusion-network-for-image-captioning","title":"Recurrent Fusion Network for Image Captioning","date":"2018-07-26","arxiv_id":"1807.09986","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-the-form-of-latent-states-in-image","title":"Rethinking the Form of Latent States in Image Captioning","date":"2018-07-26","arxiv_id":"1807.09958","repositories_listed":0,"syntology":null},{"url":null,"slug":"distinctive-attribute-extraction-for-image","title":"Distinctive-attribute Extraction for Image Captioning","date":"2018-07-25","arxiv_id":"1807.09434","repositories_listed":0,"syntology":null},{"url":null,"slug":"equal-but-not-the-same-understanding-the","title":"Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text","date":"2018-07-21","arxiv_id":"1807.08205","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-visual-localisation-factorised","title":"Inductive Visual Localisation: Factorised Training for Superior Generalisation","date":"2018-07-21","arxiv_id":"1807.08179","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-not-where-the-challenge-of","title":"What is not where: the challenge of integrating spatial representations into deep learning architectures","date":"2018-07-21","arxiv_id":"1807.08133","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-memory-trees","title":"Contextual Memory Trees","date":"2018-07-17","arxiv_id":"1807.06473","repositories_listed":0,"syntology":null},{"url":null,"slug":"factual-or-emotional-stylized-image","title":"\"Factual\" or \"Emotional\": Stylized Image Captioning with Adaptive Learning and Attention","date":"2018-07-10","arxiv_id":"1807.03871","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-sequential-temporal-information","title":"Learning The Sequential Temporal Information with Recurrent Neural Networks","date":"2018-07-08","arxiv_id":"1807.02857","repositories_listed":0,"syntology":null},{"url":null,"slug":"women-also-snowboard-overcoming-bias-in","title":"Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)","date":"2018-07-02","arxiv_id":"1807.00517","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-approach-to-pun-generation","title":"A Neural Approach to Pun Generation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"connecting-language-and-vision-to-actions","title":"Connecting Language and Vision to Actions","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-composite-metrics-for-improved","title":"Learning-based Composite Metrics for Improved Caption Evaluation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-named-entity-disambiguation-for","title":"Multimodal Named Entity Disambiguation for Noisy Social Media Posts","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"texar-a-modularized-versatile-and-extensible","title":"Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-price-of-debiasing-automatic-metrics-in-1","title":"The price of debiasing automatic metrics in natural language evalaution","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bfgan-backward-and-forward-generative","title":"BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation","date":"2018-06-21","arxiv_id":"1806.08097","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-policy-and-reward-reinforcement","title":"Multi-Level Policy and Reward Reinforcement Learning for Image Captioning","date":"2018-06-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"partially-supervised-image-captioning","title":"Partially-Supervised Image Captioning","date":"2018-06-15","arxiv_id":"1806.06004","repositories_listed":0,"syntology":null},{"url":null,"slug":"categorizing-concepts-with-basic-level-for","title":"Categorizing Concepts With Basic Level for Vision-to-Language","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dialog-generation-using-multi-turn-reasoning","title":"Dialog Generation Using Multi-Turn Reasoning Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-functional-and-geometric-bias","title":"Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-image-captions-in-arabic-using","title":"Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"bce471989e10cf76b7a2db7f71fe01e19d80a7d760d84e7bc65f16dc6a547faf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}