https://github.com/wendongbi/MMGNN
공식 코드MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution
Papers with Code 아카이브
MME
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MME is a comprehensive evaluation benchmark for multimodal large language models. It measures both perception and cognition abilities on a total of 14 subtasks, including existence, count, position, color, poster, celebrity, scene, landmark, artwork, OCR, commonsense reasoning, numerical calculation, text translation, and code reasoning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xinyusun/mme
공식 코드Masked Motion Encoding for Self-Supervised Video Representation Learning
https://github.com/yangyue5114/DME
공식 코드Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping
https://github.com/yaolinli/TimeChat-Online
공식 코드TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
https://github.com/yhy-2000/videodeepresearch
공식 코드VideoDeepResearch: Long Video Understanding With Agentic Tool Using
https://github.com/ylwhxht/l4dr
공식 코드L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection
https://github.com/DianCh/AdaContrast
https://github.com/MS-P3/code3/tree/main/internlm
https://github.com/MindSpore-scientific-2/code-14/tree/main/internlm
https://github.com/PhysGame/PhysGame
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
https://github.com/PrasannaPulakurthi/SPM
https://github.com/VisionLearningGroup/SSDA_MME
https://github.com/XinyuSun/M3Video
Masked Motion Encoding for Self-Supervised Video Representation Learning
https://github.com/evolvinglmms-lab/lmms-eval
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
https://github.com/fudandisc/reform-eval
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/huofushuo/SID
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
https://github.com/jzhang38/EasyContext
https://github.com/lastmile-ai/aiconfig
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
https://github.com/nazmul-karim170/C-SFDA_Source-Free-Domain-Adaptation
https://github.com/nvlabs/eagle
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
https://github.com/open-compass/vlmevalkit
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
https://github.com/opengvlab/internvideo2
VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation
https://github.com/opengvlab/videochat-flash
VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation
https://github.com/renshuhuai-andy/timechat
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
https://github.com/userb2020/s3d
https://github.com/yfzhang114/r1_reward
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.