https://github.com/wiktormucha/effhandegonet
공식 코드In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/winwinwenwen77/modularbvqa
공식 코드https://github.com/wiselnn570/videorope
공식 코드VideoRoPE: What Makes for Good Video Rotary Position Embedding?
https://github.com/wjn922/sp-tad
공식 코드Towards High-Quality Temporal Action Detection with Sparse Proposals
https://github.com/wjun0830/qd-detr
공식 코드Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
https://github.com/wpr001/ugc_videocaptioner
공식 코드UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
https://github.com/wsj-sjtu/fvq
공식 코드FVQ: A Large-Scale Dataset and A LMM-based Method for Face Video Quality Assessment
https://github.com/x-plug/youku-mplug
공식 코드Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
https://github.com/xiangjieSui/img2video
공식 코드Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
https://github.com/xiaogangpeng/hypervd
공식 코드Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
https://github.com/xiaoyu-1123/prefm
공식 코드PreFM: Online Audio-Visual Event Parsing via Predictive Future Modeling
https://github.com/xinghaochen/squeezetime
공식 코드No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
https://github.com/xinliyue/vqa-generalization
공식 코드Revisiting Video Quality Assessment from the Perspective of Generalization
https://github.com/xinyiW915/SPIE-2023-Supplementary
공식 코드UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment
https://github.com/xinyiw915/relax-vqa
공식 코드ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
https://github.com/xjpp2016/mavd
공식 코드Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
https://github.com/xlliu7/E2E-TAD
공식 코드https://github.com/xlliu7/TadTR
공식 코드https://github.com/xmed-lab/distillingself
공식 코드Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions
https://github.com/xqwang14/eva02-at
공식 코드https://github.com/xuyang-liu16/vidcom2
공식 코드Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
https://github.com/xyz123xyz456/hallo4
공식 코드https://github.com/yale-nlp/TOMATO
공식 코드TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
https://github.com/yale-nlp/mmvu
공식 코드MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
https://github.com/yaolinli/TimeChat-Online
공식 코드TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
https://github.com/yaolinli/vce
공식 코드Edit As You Wish: Video Caption Editing with Multi-grained User Control
https://github.com/yeliudev/R2-Tuning
공식 코드R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
https://github.com/yeliudev/VideoMind
공식 코드https://github.com/yhy-2000/videodeepresearch
공식 코드VideoDeepResearch: Long Video Understanding With Agentic Tool Using
https://github.com/yikaiw/vidu4d
공식 코드Video4DGen: Enhancing Video and 4D Generation through Mutual Optimization
https://github.com/yixuan423/compressed-face-videos-quality-assessment
공식 코드Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
https://github.com/youcanbaby/mh-detr
공식 코드MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer
https://github.com/yrcong/sttran
공식 코드Spatial-Temporal Transformer for Dynamic Scene Graph Generation
https://github.com/yueatsprograms/ttt_cifar_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/yueatsprograms/ttt_imagenet_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/yujiangpu20/pel4vad
공식 코드Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.