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Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LLaVA-VL/LLaVA-Plus-Codebase
공식 코드LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
https://github.com/LeoLee7/Directional_guidance
공식 코드Right this way: Can VLMs Guide Us to See More to Answer Questions?
https://github.com/Linvyl/DAM-QA
공식 코드Describe Anything Model for Visual Question Answering on Text-rich Images
https://github.com/LiqunChen0606/Graph-Optimal-Transport
공식 코드https://github.com/Luffy03/UniBiomed
공식 코드UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
https://github.com/LuoweiZhou/VLP
공식 코드Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/MAGIC-AI4Med/ChestX-Reasoner
공식 코드https://github.com/MILVLG/mcan-vqa
공식 코드Deep Modular Co-Attention Networks for Visual Question Answering
https://github.com/MILVLG/mmnas
공식 코드https://github.com/MalvinaNikandrou/contvqa
공식 코드Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering
https://github.com/MalvinaNikandrou/crope
공식 코드CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts
https://github.com/MalvinaNikandrou/mafed
공식 코드Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation
https://github.com/MapEval/MapEval-API
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
https://github.com/MapEval/MapEval-Textual
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
https://github.com/MapEval/MapEval-Visual
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
https://github.com/MarcBS/VIBIKNet
공식 코드VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering
https://github.com/McGill-NLP/AURORA
공식 코드Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
https://github.com/Mingxiao-Li/DMMGR
공식 코드https://github.com/NNReasoning/What-Can-Neural-Networks-Reason-About
공식 코드https://github.com/NUS-HPC-AI-Lab/SGL
공식 코드A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs
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/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/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 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.