https://github.com/NVlabs/RelViT
공식 코드RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
Papers with Code 아카이브
Visual Question Answering (VQA)
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Neel1302/o1-planning-benchmark
공식 코드On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
https://github.com/NickyFot/HitchhikersGuide
공식 코드A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
https://github.com/NoviScl/AdvRACE
공식 코드Benchmarking Robustness of Machine Reading Comprehension Models
https://github.com/OFA-Sys/ONE-PEACE
공식 코드ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
https://github.com/PKU-YuanGroup/LLaVA-CoT
공식 코드https://github.com/PKU-YuanGroup/MoE-LLaVA
공식 코드MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
https://github.com/PKU-YuanGroup/Video-LLaVA
공식 코드Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout
공식 코드ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
https://github.com/PaddlePaddle/Research/tree/master/NLP/EMNLP2019-AKGCM
공식 코드Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
https://github.com/Panda-Peter/image-captioning
공식 코드https://github.com/Paranioar/UniPT
공식 코드UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
https://github.com/Park-ing-lot/Imagine
공식 코드https://github.com/PhoebusSi/SAR
공식 코드Check It Again: Progressive Visual Question Answering via Visual Entailment
https://github.com/PierreMarza/teaching_agents_how_to_map
공식 코드Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
https://github.com/PlusLabNLP/Re-ReST
공식 코드Re-ReST: Reflection-Reinforced Self-Training for Language Agents
https://github.com/Pointcept/Pointcept
공식 코드Sonata: Self-Supervised Learning of Reliable Point Representations
https://github.com/PongNJ/V-RoAst
공식 코드V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?
https://github.com/Q-Future/Q-Bench
공식 코드https://github.com/QualityAssessment/DOVER
공식 코드https://github.com/QualityAssessment/DisCoVQA
공식 코드DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
https://github.com/QualityAssessment/FAST-VQA-and-FasterVQA
공식 코드Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
https://github.com/RobBlackwell/can-llms-reason-about-the-rcc
공식 코드Can Large Language Models Reason about the Region Connection Calculus?
https://github.com/RunpeiDong/DreamLLM
공식 코드https://github.com/SALT-NLP/LLaVAR
공식 코드LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
https://github.com/SHI-Labs/Interpretable-Visual-Reasoning
공식 코드https://github.com/SJTUHaiyangYu/BackdoorMBTI
공식 코드BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
https://github.com/SPRIGHT-T2I/SPRIGHT
공식 코드Getting it Right: Improving Spatial Consistency in Text-to-Image Models
https://github.com/SRI-CSL/TrinityMultimodalTrojAI
공식 코드https://github.com/SUTDCV/SUTD-TrafficQA
공식 코드https://github.com/SaMMyCHoo/Light-VQA-plus
공식 코드Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance
https://github.com/Seth-Park/MultimodalExplanations
공식 코드Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
https://github.com/ShannonAI/ChineseBert
공식 코드ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/ShannonAI/CorefQA
공식 코드https://github.com/ShannonAI/dice_loss_for_NLP
공식 코드https://github.com/ShannonAI/mrc-for-dependency-parsing
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.