Papers with Code 아카이브
Zero-Shot Video Question Answer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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This task present the results of Zeroshot Question Answer results on TGIF-QA dataset for LLM powered Video Conversational Models.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kkahatapitiya/langrepo
공식 코드https://github.com/leon1207/video-rag-master
공식 코드Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
https://github.com/magic-research/PLLaVA
공식 코드PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
https://github.com/mbzuai-oryx/video-chatgpt
공식 코드Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/mbzuai-oryx/videogpt-plus
공식 코드VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
https://github.com/md-mohaiminul/BIMBA
공식 코드BIMBA: Selective-Scan Compression for Long-Range Video Question Answering
https://github.com/md-mohaiminul/VideoRecap
공식 코드https://github.com/mistralai/mistral-src
공식 코드https://github.com/mit-han-lab/llm-awq
공식 코드https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/nvlabs/vila
공식 코드https://github.com/opengvlab/ask-anything
공식 코드https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/panasonicconnect/videomultiagents
공식 코드VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
https://github.com/pku-yuangroup/chat-univi
공식 코드https://github.com/qhuni/deve-qa
공식 코드https://github.com/qwenlm/qwen2-vl
공식 코드Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/renshuhuai-andy/timechat
공식 코드TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/rese1f/MovieChat
공식 코드MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
https://github.com/rikeilong/bay-cat
공식 코드CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
https://github.com/rupertluo/valley
공식 코드Valley: Video Assistant with Large Language model Enhanced abilitY
https://github.com/shajiayu1/OmniDataComposer
공식 코드OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
https://github.com/thanhnguyentang/neural-offline-rl
공식 코드VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
https://github.com/tingyu215/ts-llava
공식 코드https://github.com/traveler-framework/traveler
공식 코드TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering
https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
https://github.com/yeliudev/VideoMind
공식 코드https://github.com/yui010206/sevila
공식 코드Self-Chained Image-Language Model for Video Localization and Question Answering
https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/Mind23-2/MindCode-140
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
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/MindSpore-scientific-2/code-14/tree/main/llava_next
https://github.com/OpenGVLab/TimeSuite
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.