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Visual Commonsense Reasoning

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Papers

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Showing 1–50 of 65 papers

TitleStatusHype
Compositional Image-Text Matching and Retrieval by Grounding EntitiesCode0
Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning DistractorCode0
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition—0
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language ModelsCode2
Do Vision-Language Transformers Exhibit Visual Commonsense? An Empirical Study of VCR—0
EventLens: Leveraging Event-Aware Pretraining and Cross-modal Linking Enhances Visual Commonsense Reasoning—0
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual PromptsCode0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling—0
ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models—0
A Survey on Interpretable Cross-modal ReasoningCode1
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—0
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions—0
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Learning to Agree on Vision Attention for Visual Commonsense Reasoning—0
Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning—0
Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement LearningCode1
VASR: Visual Analogies of Situation RecognitionCode0
A survey on knowledge-enhanced multimodal learning—0
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question AnsweringCode2
ILLUME: Rationalizing Vision-Language Models through Human InteractionsCode0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks—0
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks—0
Attention Mechanism based Cognition-level Scene Understanding—0
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language TransformersCode0
All in One: Exploring Unified Video-Language Pre-trainingCode2
Joint Answering and Explanation for Visual Commonsense ReasoningCode0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound—0
SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning—0
Towards artificial general intelligence via a multimodal foundation modelCode1
Broaden the Vision: Geo-Diverse Visual Commonsense ReasoningCode1
X-modaler: A Versatile and High-performance Codebase for Cross-modal AnalyticsCode1
Interpretable Visual Understanding with Cognitive Attention NetworkCode0
Cognitive Visual Commonsense Reasoning Using Dynamic Working MemoryCode0
MERLOT: Multimodal Neural Script Knowledge ModelsCode1
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues—0
Playing Lottery Tickets with Vision and Language—0
Unifying Vision-and-Language Tasks via Text GenerationCode1
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning—0
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense GraphsCode1
To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI—0
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
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