https://github.com/AIM-SKKU/QA-TIGER
공식 코드Question-Aware Gaussian Experts for Audio-Visual Question Answering
Papers with Code 아카이브
Multimodal Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Reasoning over multimodal inputs.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CASOS-IDeaS-CMU/SegSub
공식 코드SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
https://github.com/Darcyddx/Video-LLM
공식 코드https://github.com/Fancy-MLLM/R1-onevision
공식 코드R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
https://github.com/IVGSZ/Flash-VStream
공식 코드Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V
공식 코드ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
https://github.com/LanceZPF/MDK12
공식 코드https://github.com/Letian2003/MM_INF
공식 코드Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
https://github.com/NVlabs/FRAG
공식 코드FRAG: Frame Selection Augmented Generation for Long Video and Long Document Understanding
https://github.com/Nithin-Holla/meme_challenge
공식 코드https://github.com/PKU-YuanGroup/LLaVA-CoT
공식 코드https://github.com/SkyworkAI/Skywork-R1V
공식 코드Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
https://github.com/THUNLP-MT/EscapeCraft
공식 코드https://github.com/Twilight92z/Role-of-LLM
공식 코드Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
https://github.com/Yui010206/CREMA
공식 코드CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion
https://github.com/Zhangwenyao1/DreamVLA
공식 코드DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
https://github.com/Ziyang412/VideoTree
공식 코드VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos
https://github.com/agi-edgerunners/iil
공식 코드https://github.com/aibee00/socraticquestioning
공식 코드Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild
https://github.com/aimagelab/LLaVA-MORE
공식 코드LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning
https://github.com/aipenguin/stopreasoning
공식 코드Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image
https://github.com/antresearchnlp/vilasr
공식 코드Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
https://github.com/aurora-slz/mm-verify
공식 코드MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
https://github.com/b08202033/SAKURA
공식 코드https://github.com/bradyfu/awesome-multimodal-large-language-models
공식 코드A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
https://github.com/bytedance/pargo
공식 코드MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/ceezh/silvr
공식 코드https://github.com/chartgalaxy/chartgalaxy
공식 코드ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
https://github.com/chengtan9907/mc-cot
공식 코드https://github.com/ckyang1124/sakura
공식 코드https://github.com/comeandcode/MRT
공식 코드Re-Imagining Multimodal Instruction Tuning: A Representation View
https://github.com/cvndsh/rebus
공식 코드REBUS: A Robust Evaluation Benchmark of Understanding Symbols
https://github.com/czhhzc/CoMT
공식 코드CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models
https://github.com/danielabd/apollo-cap
공식 코드Apollo: Zero-shot MultiModal Reasoning with Multiple Experts
https://github.com/dcdmllm/cheetah
공식 코드Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
https://github.com/dcdmllm/hyperllava
공식 코드HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.