Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/mv-lab/videoai-speedrun
공식 코드AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
https://github.com/mv-lab/youtube8m-19
공식 코드Multi-attention Networks for Temporal Localization of Video-level Labels
https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/naver-ai/tc-clip
공식 코드Leveraging Temporal Contextualization for Video Action Recognition
https://github.com/neelumadan/vifm_survey
공식 코드https://github.com/nercms-mmap/friendsqa
공식 코드https://github.com/netflix/videoannotator
공식 코드https://github.com/newbeeyoung/video-corruption-robustness
공식 코드Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
https://github.com/newsdata/SoccerDB
공식 코드SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
https://github.com/nikkiwoo-gh/ITV
공식 코드https://github.com/noureldien/timeception
공식 코드https://github.com/nrasajski/behave
공식 코드https://github.com/nus-cvml/awesome-temporal-action-segmentation
공식 코드Temporal Action Segmentation: An Analysis of Modern Techniques
https://github.com/nvlabs/vila
공식 코드LongVILA: Scaling Long-Context Visual Language Models for Long Videos
https://github.com/om-ai-lab/OmAgent
공식 코드OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
https://github.com/ombretta/longterm_datasets
공식 코드Are current long-term video understanding datasets long-term?
https://github.com/omron-sinicx/com_kitchens
공식 코드COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
https://github.com/open-compass/vlmevalkit
공식 코드MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
https://github.com/open-mmlab/foleycrafter
공식 코드FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds
https://github.com/opengvlab/ask-anything
공식 코드https://github.com/opengvlab/ego4d-eccv2022-solutions
공식 코드InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
https://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/opengvlab/internvl
공식 코드https://github.com/opengvlab/video-mamba-suite
공식 코드Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
https://github.com/opengvlab/videomamba
공식 코드VideoMamba: State Space Model for Efficient Video Understanding
https://github.com/openrobotlab/egohod
공식 코드Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning
https://github.com/openvinotoolkit/training_extensions
공식 코드https://github.com/orhir/stg-nf
공식 코드https://github.com/orrzohar/Video-STaR
공식 코드Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
https://github.com/ouyangkun10/spacer
공식 코드https://github.com/ouyangkun10/spatial-r1
공식 코드https://github.com/panasonicconnect/videomultiagents
공식 코드VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
https://github.com/park-jungin/byov
공식 코드https://github.com/park-jungin/dualpath
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.