Awesome Vision And Language Save
A curated list of awesome vision and language resources (still under construction... stay tuned!)
Project README
Awesome Vision-and-Language: 
A curated list of awesome vision and language resources, inspired by awesome-computer-vision.
Table Of Contents
- Survey
- Dataset
- Image Captioning
- Image Retrieval
- Scene Text Recognition (OCR)
- Scene Graph
- text2image
- Video Captioning
- Video Question Answering
- Video Understanding
- Vision and Language Navigation
- Vision and Language Pretraining
- Visual Dialog
- Visual Grounding
- Visual Question Answering (VQA)
- Visual Reasoning
- Visual Relationship Detection
- Visual Storytelling
Survey
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| A Survey of Current Datasets for Vision and Language Research | 2015 EMNLP | 1506.06833 | ||
| Multimodal Machine Learning: A Survey and Taxonomy | 1705.09406 | |||
| A Comprehensive Survey of Deep Learning for Image Captioning | 1810.04020 | |||
| Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods | 1907.09358 | |||
| A Survey of Scene Graph Generation and Application | Scene-Graph-Survey | |||
| Challenges and Prospects in Vision and Language Research | 1904.09317 | |||
| Deep Multimodal Representation Learning: A Survey | 2019 ACCESS | ACCESS 2019 | ||
| Multimodal Intelligence: Representation Learning, Information Fusion, and Applications | 1911.03977 | |||
| Vision and Language: from Visual Perception to Content Creation | 2020 APSIPA | 1912.11872 | ||
| Multimodal Research in Vision and Language: A Review of Current and Emerging Trends | 2010.09522 | |||
Dataset
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| VQA: Visual Question Answering | 2015 ICCV | 1505.00468 | visualqa | |
| Visual Storytelling | 2016 NAACL | 1604.03968 | ai-visual-storytelling-seq2seq | VIST |
| Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations | 2017 IJCV | 1602.07332 | visual_genome_python_driver | visualgenome |
| CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning | 2017 CVPR | 1612.06890 | ||
| AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions | 2018 CVPR | 1705.08421 | AVA | |
| Embodied Question Answering | 2018 CVPR | 1711.11543 | embodiedqa | |
| Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments | 2018 CVPR | 1711.07280 | bringmeaspoon | |
| GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering | 2019 CVPR | 1902.09506 | visualreasoning | |
| From Recognition to Cognition: Visual Commonsense Reasoning | 2019 CVPR | 1811.10830 | r2c | VCR |
| VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research | 2019 ICCV | 1904.03493 | ||
| Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning | 2020 NeurIPS | 2010.00763 | Bongard-LOGO | |
| Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions | 2022 CVPR | 2205.13803 | Bongard-HOI | |
Image Captioning
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Long-term Recurrent Convolutional Networks for Visual Recognition and Description | 2015 CVPR | 1411.4389 | ||
| Deep Visual-Semantic Alignments for Generating Image Descriptions | 2015 CVPR | 1412.2306 | ||
| Show and Tell A Neural Image Caption Generator | 2015 CVPR | 1411.4555 | show_and_tell.tensorflow | |
| Show, Attend and Tell Neural Image Caption Generation with Visual Attention | 2015 ICML | 1502.03044 | show-attend-and-tell | |
| From Captions to Visual Concepts and Back | 2015 CVPR | 1411.4952 | visual-concepts | |
| Image Captioning with Semantic Attention | 2016 CVPR | 1603.03925 | semantic-attention | |
| Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning | 2017 CVPR | 1612.01887 | AdaptiveAttention | |
| Self-critical Sequence Training for Image Captioning | 2017 CVPR | 1612.00563 | ||
| A Hierarchical Approach for Generating Descriptive Image Paragraphs | 2017 CVPR | 1611.06607 | ||
| Deep reinforcement learning-based image captioning with embedding reward | 2017 CVPR | 1704.03899 | ||
| Semantic compositional networks for visual captioning | 2017 CVPR | 1611.08002 | Semantic_Compositional_Nets | |
| StyleNet: Generating Attractive Visual Captions with Styles | 2017 CVPR | CVPR 2017 | stylenet | |
| Training for Diversity in Image Paragraph Captioning | 2018 EMNLP | ENNLP 2018 | image-paragraph-captioning | |
| Neural Baby Talk | 2018 CVPR | 1803.09845 | NeuralBabyTalk | |
| Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering | 2018 CVPR | 1707.07998 | ||
| “Factual” or “Emotional”: Stylized Image Captioning with Adaptive Learning and Attention | 2018 ECCV | 1807.03871 | ||
| Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation | 2019 AAAI | 1805.08191 | ||
| Unsupervised Image Captioning | 2019 CVPR | 1811.10787 | unsupervised_captioning | |
| Context-aware visual policy network for fine-grained image captioning | 2019 TPAMI | 1906.02365 | CAVP | |
| Dense Relational Captioning Triple-Stream Networks for Relationship-Based Captioning | 2019 CVPR | 1903.05942 | ||
| Describing like Humans on Diversity in Image Captioning | 2019 CVPR | 1903.12020 | ||
| Good News, Everyone! Context driven entity-aware captioning for news images | 2019 CVPR | 1904.01475 | ||
| Auto-Encoding Scene Graphs for Image Captioning | 2019 CVPR | 1812.02378 | SGAE | |
| Unsupervised Image Captioning | 2019 CVPR | 1811.10787 | unsupervised_captioning | |
| MSCap: Multi-Style Image Captioning with Unpaired Stylized Text | 2019 CVPR | CVPR 2019 | ||
| Robust Change Captioning | 2019 ICCV | 1901.02527 | ||
| Attention on Attention for Image Captioning | 2019 ICCV | 1908.06954 | ||
| Context-Aware Group Captioning via Self-Attention and Contrastive Features | 2020 CVPR | 2004.03708 | ||
| Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs | 2020 CVPR | 2003.00387 | asg2cap | |
| Comprehensive Image Captioning via Scene Graph Decomposition | 2020 ECCV | 2007.11731 | Sub-GC | |
| Are scene graphs good enough to improve Image Captioning? | 2020 AACL | 2009.12313 | ||
| SG2Caps: Revisiting Scene Graphs for Image Captioning | 2021 arxiv | 2102.04990 | ||
Image Retrieval
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Visual Word2Vec (vis-w2v) Learning Visually Grounded Word Embeddings Using Abstract Scenes | 2016 CVPR | 1511.07067 | VisualWord2Vec | |
| Composing Text and Image for Image Retrieval - An Empirical Odyssey | 2019 CVPR | 1812.07119 | tirg | |
| Learning Relation Alignment for Calibrated Cross-modal Retrieval | 2021 ACL | 2105.13868 | IAIS | |
| ImageCoDe: Image Retrieval from Contextual Descriptions | 2022 ACL | 2203.15867 | ImageCoDe | |
Scene Text Recognition
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Towards Unconstrained End-to-End Text Spotting | 2019 ICCV | 1908.09231 | ||
| What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis | 2019 ICCV | 1904.01906 | clovaai | |
Scene Graph
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Image Retrieval Using Scene Graphs | 2015 CVPR | 7298990 | ||
| Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations | 2017 IJCV | 1602.07332 | visual_genome_python_driver | visualgenome |
| Scene Graph Generation by Iterative Message Passing | 2017 CVPR | 1701.02426 | scene-graph-TF-release | |
| Scene Graph Generation from Objects, Phrases and Region Captions | 2017 ICCV | 1707.09700 | MSDN | |
| Neural Motifs: Scene Graph Parsing with Global Context | 2018 CVPR | 1711.06640 | neural-motifs | |
| Generating Triples with Adversarial Networks for Scene Graph Construction | 2018 AAAI | 1802.02598 | ||
| LinkNet: Relational Embedding for Scene Graph | 2018 NIPS | 1811.06410 | ||
| Image Generation from Scene Graphs | 2018 CVPR | 1804.01622 | sg2im | |
| Graph R-CNN for Scene Graph Generation | 2018 ECCV | 1808.00191 | graph-rcnn.pytorch | |
| Scene Graph Generation with External Knowledge and Image Reconstruction | 2019 CVPR | 1904.00560 | ||
| Specifying Object Attributes and Relations in Interactive Scene Generation | 2019 ICCV | 1909.05379 | scene_generation | |
| Attentive Relational Networks for Mapping Images to Scene Graphs | 2019 CVPR | 1811.10696 | ||
| Exploring Context and Visual Pattern of Relationship for Scene Graph Generation | 2019 CVPR | sceneGraph_Mem | ||
| Graphical Contrastive Losses for Scene Graph Parsing | 2019 CVPR | 1903.02728 | ContrastiveLosses4VRD | |
| Knowledge-Embedded Routing Network for Scene Graph Generation | 2019 CVPR | 1903.03326 | KERN | |
| Learning to Compose Dynamic Tree Structures for Visual Contexts | 2019 CVPR | 1812.01880 | VCTree | |
| Counterfactual Critic Multi-Agent Training for Scene Graph Generation | 2019 ICCV | 1812.02347 | ||
| Scene Graph Prediction with Limited Labels | 2019 ICCV | 1904.11622 | limited-label | |
| Unbiased Scene Graph Generation from Biased Training | 2020 CVPR | 2002.11949 | Scene-Graph-Benchmark | |
| GPS-Net Graph Property Sensing Network for Scene Graph Generation | 2020 CVPR | 2003.12962 | GPS-Net | |
| Learning Visual Commonsense for Robust Scene Graph Generation | 2020 ECCV | 2006.09623 | ||
| Sketching Image Gist Human-Mimetic Hierarchical Scene Graph Generation | 2020 ECCV | 2007.08760 | het-eccv20 | |
text2image
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Generative Adversarial Text to Image Synthesis | 2016 ICML | 1605.05396 | icml2016 | |
| StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks | 2017 ICCV | 1612.03242 | StackGAN | |
| AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | 2018 CVPR | 1711.10485 | AttnGAN | |
| Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network | 2018 CVPR | 1802.09178 | HDGan | |
| StoryGAN: A Sequential Conditional GAN for Story Visualization | 2019 CVPR | 1812.02784 | StoryGAN | |
| MirrorGAN: Learning Text-to-image Generation by Redescription | 2019 CVPR | 1903.05854 | ||
| DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis | 2019 CVPR | 1904.01310 | ||
| Semantics Disentangling for Text-to-Image Generation | 2019 CVPR | 1904.01480 | ||
| Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction | 2019 ICCV | 1811.09845 | GeNeVA | |
| Specifying Object Attributes and Relations in Interactive Scene Generation | 2019 ICCV | 1909.05379 | scene_generation | |
Video Captioning
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Long-term Recurrent Convolutional Networks for Visual Recognition and Description | 2015 CVPR | 1411.4389 | ||
| Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks | 2016 CVPR | 1510.07712 | ||
| Attention-Based Multimodal Fusion for Video Description | 2017 CVPR | 1701.03126 | ||
| Semantic compositional networks for visual captioning | 2017 CVPR | 1611.08002 | ||
| Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description | 2017 CVPR | CVPR_2017 | ||
| Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning | 2018 CVPR | 1804.00100 | ||
| Adversarial Inference for Multi-Sentence Video Description | 2019 CVPR | 1812.05634 | adv-inf | |
| Streamlined Dense Video Captioning | 2019 CVPR | 1904.03870 | DenseVideoCaptioning | |
| Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning | 2019 CVPR | 1906.04375 | ||
| iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | 2021 WACV | 2011.07735 | iPerceive | |
Video Question Answering
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Movieqa: Understanding stories in movies through question-answering | 2016 CVPR | 1512.02902 | MovieQA | |
| TVQA: Localized, Compositional Video Question Answering | 2018 EMNLP | 1809.01696 | TVQA | |
| Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions | 2020 ECCV | 2007.08751 | ROLL-VideoQA | |
| iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | 2021 WACV | 2011.07735 | iPerceive |
Video Understanding
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| TSM: Temporal Shift Module for Efficient Video Understanding | 2019 ICCV | 1811.08383 | temporal-shift-module | |
| A Graph-Based Framework to Bridge Movies and Synopses | 2019 ICCV | 1910.11009 | ||
Vision and Language Navigation
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Embodied Question Answering | 2018 CVPR | 1711.11543 | embodiedqa | |
| Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments | 2018 CVPR | 1711.07280 | bringmeaspoon | |
Vision-and-Language Pretraining
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| LXMERT: Learning Cross-Modality Encoder Representations from Transformers | 2019 EMNLP | 1908.07490 | lxmert | |
| VideoBERT: A Joint Model for Video and Language Representation Learning | 2019 ICCV | 1904.01766 | ||
| ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks | 2019 NIPS | vilbert | ||
| OmniNet: A unified architecture for multi-modal multi-task learning | 2019 arxiv | 1907.07804 | OmniNet | |
| Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training | 2020 AAAI | 1908.06066 | Unicoder | |
| Unified Vision-Language Pre-Training for Image Captioning and VQA | 2020 AAAI | 1909.11059 | VLP | |
| Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks | 2020 ECCV | 1911.11237 | Oscar | |
| Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | 2020 NIPS | 2006.09882 | swav | |
| Learning to Learn Words from Visual Scenes | 2020 ECCV | 2004.06165 | Oscar | |
| ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs | 2021 AAAI | 2006.16934 | ERNIE | |
| VinVL: Revisiting Visual Representations in Vision-Language Models | 2021 CVPR | 2101.00529 | VinVL | |
| VirTex: Learning Visual Representations from Textual Annotations | 2021 CVPR | 2006.06666 | virtex | |
| Learning Transferable Visual Models From Natural Language Supervision | 2021 arxiv | 2103.00020 | ||
| Pretrained Transformers As Universal Computation Engines | 2021 arxiv | 2103.05247 | universal-computation | |
| Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision | 2021 arxiv | 2102.05918 | ||
| Self-supervised Pretraining of Visual Features in the Wild | 2021 arxiv | 2103.01988 | ||
| Transformer is All You Need Multimodal Multitask Learning with a Unified Transformer | 2021 arxiv | 2102.10772 | ||
| Zero-Shot Text-to-Image Generation | 2021 arxiv | 2102.12092 | ||
| WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training | 2021 arxiv | 2103.06561 | ||
| Improved baselines for vision-language pre-training | 2023 arxiv | 2305.08675 | ||
Visual Dialog
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Visual Dialog | 2017 CVPR | 1611.08669 | visdial | visualdialog |
| Two Can Play This Game: Visual Dialog With Discriminative Question Generation and Answering | 2018 CVPR | 1803.11186 | ||
| Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation | 2023 | 2303.05983 | ATVC | |
Visual Grounding
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Modeling Relationships in Referential Expressions with Compositional Modular Networks | 2017 CVPR | 1611.09978 | cmn | |
| Phrase Localization Without Paired Training Examples | 2019 ICCV | 1908.07553 | ||
| Learning to Assemble Neural Module Tree Networks for Visual Grounding | 2019 ICCV | 1812.03299 | ||
| A Fast and Accurate One-Stage Approach to Visual Grounding | 2019 ICCV | 1908.06354 | ||
| Zero-Shot Grounding of Objects from Natural Language Queries | 2019 ICCV | 1908.07129 | zsgnet | |
| Collaborative Transformers for Grounded Situation Recognition | 2022 CVPR | 2203.16518 | CoFormer | |
Visual Question Answering
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| VQA: Visual Question Answering | 2015 ICCV | 1505.00468 | visualqa | |
| Hierarchical question-image co-attention for visual question answering | 2016 NIPS | 1606.00061 | HieCoAttenVQA | |
| Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding | 2016 EMNLP | 1606.01847 | vqa-mcb | |
| Stacked Attention Networks for Image Question Answering | 2016 CVPR | 1511.02274 | imageqa-san | |
| Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering | 2016 ECCV | 1511.05234 | AAAA | |
| Dynamic Memory Networks for Visual and Textual Question Answering | 2016 ICML | 1603.01417 | dmn-plus | |
| Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding | 2016 EMNLP | 1606.01847 | vqa-mcb | |
| Multimodal Residual Learning for Visual QA | 2016 NIPS | 1606.01455 | nips-mrn-vqa | |
| Graph-Structured Representations for Visual Question Answering | 2017 CVPR | 1609.05600 | ||
| Making the V in VQA Matter Elevating the Role of Image Understanding in Visual Question Answering | 2017 CVPR | 1612.00837 | ||
| Learning to Reason: End-to-End Module Networks for Visual Question Answering | 2017 ICCV | 1704.05526 | ||
| Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering | 2018 AAAI | 1803.08896 | PSLQA | |
| Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering | 2018 CVPR | 1707.07998 | ||
| Tips and Tricks for Visual Question Answering Learnings from the 2017 Challenge | 2018 CVPR | 1708.02711 | vqa-winner | |
| Transfer Learning via Unsupervised Task Discovery for Visual Question Answering | 2019 CVPR | 1810.02358 | VQA-Transfer-ExternalData | |
| GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering | 2019 CVPR | 1902.09506 | visualreasoning | |
| Towards VQA Models That Can Read | 2019 CVPR | 1904.08920 | ||
| From Strings to Things: Knowledge-enabled VQA Model that can Read and Reason | 2019 ICCV | ICCV2019 | ||
| An Empirical Study on Leveraging Scene Graphs for Visual Question Answering | 2019 BMVC | 1907.12133 | scene-graphs-vqa | |
| RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning | 2022 ICLR | 2204.11167 | RelViT | |
| TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation | 2022 arXiv | 2208.01813 | TAG | |
Visual Reasoning
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning | 2017 CVPR | 1612.06890 | ||
| Inferring and Executing Programs for Visual Reasoning | 2017 ICCV | 1705.03633 | ||
| GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering | 2019 CVPR | 1902.09506 | visualreasoning | |
| Explainable and Explicit Visual Reasoning over Scene Graphs | 2019 CVPR | 1812.01855 | ||
| From Recognition to Cognition: Visual Commonsense Reasoning | 2019 CVPR | 1811.10830 | r2c | VCR |
| Dynamic Graph Attention for Referring Expression Comprehension | 2019 ICCV | 1909.08164 | ||
| Visual Semantic Reasoning for Image-Text Matching | 2019 ICCV | 1909.02701 | VSRN | |
| Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning | 2020 NeurIPS | 2010.00763 | Bongard-LOGO | |
| Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions | 2022 CVPR | 2205.13803 | Bongard-HOI | |
| RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning | 2022 ICLR | 2204.11167 | RelViT | |
| PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization | 2023 ICCV | 2307.15199 | PromptStyler | |
Visual Relationship Detection
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Visual Relationship Detection with Language Priors | 2016 ECCV | 1608.00187 | Visual-Relationship-Detection | |
| ViP-CNN: Visual Phrase Guided Convolutional Neural Network | 2017 CVPR | 1702.07191 | ||
| Visual Translation Embedding Network for Visual Relation Detection | 2017 CVPR | 1702.08319 | drnet | |
| Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection | 2017 CVPR | 1703.03054 | DeepVariationRL | |
| Detecting Visual Relationships with Deep Relational Networks | 2017 CVPR | 1704.03114 | drnet | |
| Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues | 2017 ICCV | 1611.06641 | pl-clc | |
| Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation | 2017 ICCV | 1707.09423 | ||
| Referring Relationships | 2018 CVPR | 1803.10362 | ReferringRelationships | |
| Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition | 2018 ECCV | 1807.04979 | ZoomNet | |
| Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features | 2018 ECCV | 1808.00171 | vrd | |
| Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships | 2020 ICLR | 1910.12324 | ||
Visual Storytelling
| Title | Conference / Journal | Paper | Code | Remarks |
|---|---|---|---|---|
| Visual Storytelling | 2016 NAACL | 1604.03968 | visual_genome_python_driver | VIST |
| No Metrics Are Perfect Adversarial Reward Learning for Visual Storytelling | 2018 ACL | 1804.09160 | AREL | |
| Show, Reward and Tell: Automatic Generation of Narrative Paragraph from Photo Stream by Adversarial Training | 2018 AAAI | |||
| Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling | 2020 AAAI | 2002.00774 | ||
| Storytelling from an Image Stream Using Scene Graphs | 2020 AAAI | AAAI 2020 | ||
Contributing
Please feel free to send me pull requests or email ([email protected]) to add links.
Licenses
License
To the extent possible under law, Sangmin Woo has waived all copyright and related or neighboring rights to this work.
Open Source Agenda is not affiliated with "Awesome Vision And Language" Project. README Source: sangminwoo/awesome-vision-and-language
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