Papers with Code 아카이브
Zero-Shot Video Question Answer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 17
- 보존된 평가 기록
- 239
This task present the results of Zeroshot Question Answer results on TGIF-QA dataset for LLM powered Video Conversational Models.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering
ST-LLM: Large Language Models Are Effective Temporal Learners
An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
Elysium: Exploring Object-level Perception in Videos via MLLM
Understanding Long Videos with Multimodal Language Models
InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
Language Repository for Long Video Understanding
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Video ReCap: Recursive Captioning of Hour-Long Videos
Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
A Simple LLM Framework for Long-Range Video Question-Answering
Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
VILA: On Pre-training for Visual Language Models
Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
Zero-Shot Video Question Answering with Procedural Programs
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
Vamos: Versatile Action Models for Video Understanding
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Mistral 7B
BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Valley: Video Assistant with Large Language model Enhanced abilitY
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.