https://github.com/jpthu17/dicosa
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Papers with Code 아카이브
Video Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 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/jpthu17/diffusionret
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/juruobenruo/DexVLA
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/jy0205/lavit
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
https://github.com/jzhang38/EasyContext
https://github.com/kahnchana/locvlm
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
https://github.com/knowlab/bi-weekly-paper-presentation
https://github.com/kyegomez/PALI
PaLI-X: On Scaling up a Multilingual Vision and Language Model
https://github.com/lastmile-ai/aiconfig
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
https://github.com/linjieli222/hero_video_feature_extractor
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
https://github.com/llava-annonymous/llava
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/lucidrains/TimeSformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/lucidrains/flamingo-pytorch
https://github.com/lucidrains/mammut-pytorch
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
https://github.com/m-bain/video-transformers
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mansigoel/TVQA
https://github.com/md-mohaiminul/objectstatechange
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mgmalek/efficient_cross_entropy
https://github.com/mlfoundations/open_flamingo
https://github.com/modelscope/modelscope
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/moh833/VQA
https://github.com/mx-mark/videotransformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/ninglab/ecellm
https://github.com/nokia-bell-labs/papagei-foundation-model
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
https://github.com/onue5/VideoQA
https://github.com/open-mmlab/mmaction2
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/openbmb/minicpm-v
https://github.com/opengvlab/llama-adapter
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/pku-yuangroup/languagebind
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/pku-yuangroup/video-bench
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4
https://github.com/pwc-1/Paper-9/tree/main/2/mistral
https://github.com/pwc-1/Paper-9/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.