https://github.com/wangjk666/omnivid
공식 코드OmniVid: A Generative Framework for Universal Video Understanding
Papers with Code 아카이브
Video Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 2
- 평가표
- 28
- 보존된 평가 기록
- 254
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wayveai/lingoqa
공식 코드https://github.com/whb139426/gcg
공식 코드https://github.com/whwu95/freeva
공식 코드https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
https://github.com/xijun-cs/vila
공식 코드ViLA: Efficient Video-Language Alignment for Video Question Answering
https://github.com/xiuyuan-chen/autoeval-video
공식 코드https://github.com/xliu443/tem-adapter
공식 코드Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer
https://github.com/xudonglinthu/upgradable-multimodal-intelligence
공식 코드Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval
https://github.com/xuehy/TreeAttention
공식 코드A Better Way to Attend: Attention with Trees for Video Question Answering
https://github.com/yangliu9208/vcsr
공식 코드https://github.com/yeliudev/VideoMind
공식 코드https://github.com/yellow-binary-tree/STAIR
공식 코드STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering
https://github.com/yellow-binary-tree/hawkeye
공식 코드HawkEye: Training Video-Text LLMs for Grounding Text in Videos
https://github.com/yellow-binary-tree/mmduet
공식 코드https://github.com/yl3800/eigv
공식 코드Equivariant and Invariant Grounding for Video Question Answering
https://github.com/yl3800/igv
공식 코드https://github.com/yl3800/transtr
공식 코드Discovering Spatio-Temporal Rationales for Video Question Answering
https://github.com/yui010206/sevila
공식 코드Self-Chained Image-Language Model for Video Localization and Question Answering
https://github.com/yukw777/tglg
공식 코드Temporally-Grounded Language Generation: A Benchmark for Real-Time Vision-Language Models
https://github.com/zchoi/vcrn
공식 코드Visual Commonsense-aware Representation Network for Video Captioning
https://github.com/zengyan-97/x2-vlm
공식 코드X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/zhiyuanhubj/long_form_videoqa
공식 코드Encoding and Controlling Global Semantics for Long-form Video Question Answering
https://github.com/zhousheng97/egotextvqa
공식 코드EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
https://github.com/zhousheng97/vitxt-gqa
공식 코드Scene-Text Grounding for Text-Based Video Question Answering
https://github.com/ziplab/longvlm
공식 코드LongVLM: Efficient Long Video Understanding via Large Language Models
https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2024-MindSpore-1/Code5/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2024-MindSpore-1/Code6/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/BM-K/Question-Difficulty-Estimation
https://github.com/Lee-Ft/RHA
Relation-aware Hierarchical Attention Framework for Video Question Answering
https://github.com/MCG-NJU/VideoChat-Online
https://github.com/MS-P3/code3/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/MarkHershey/arxiv-dl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.