https://github.com/Mind23-2/MindCode-140
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Papers with Code 아카이브
Video Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindCode-4/code-4/tree/main/qwen2_moe
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindSpore-scientific-2/code-12/tree/main/isr
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
https://github.com/MindSpore-scientific-2/code-14/tree/main/llava_next
https://github.com/OpenGVLab/TimeSuite
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
https://github.com/PKU-YuanGroup/LLMBind
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PKU-YuanGroup/MoE-LLaVA
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/QwenLM/Qwen2.5-Omni
https://github.com/The-AI-Summer/self-attention-cv
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/TraffiX-VideoQA/TUMTraffic-VideoQA-Baseline
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/XLearning-SCU/2024-ICLR-Norton
Multi-granularity Correspondence Learning from Long-term Noisy Videos
https://github.com/ZhangYiqun018/StickerConv
https://github.com/abhigoyal1997/CS-763-Project
https://github.com/adacheng/egothink
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
https://github.com/antoine77340/howto100m
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/baichuan-inc/Baichuan-Omni-1.5
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/camenduru/llava-colab
https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
https://github.com/damo-nlp-sg/inf-clip
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
https://github.com/davide-coccomini/TimeSformer-Video-Classification
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/dinhvietcuong1996/icme25-inova
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/doc-doc/NExT-OE
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
https://github.com/facebookresearch/fairseq2
https://github.com/h19920918/quiz_for_day06
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/happen2me/cross-gnn
https://github.com/hcplab-sysu/causal-vlreasoning
https://github.com/huggingface/transformers
https://github.com/jayleicn/TVQA
https://github.com/jerrywn121/TianChi_AIEarth
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/joseponlee/intentqa
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.