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/jiaxing-star/facialdynamic
공식 코드https://github.com/jindongjiang/slotssms
공식 코드https://github.com/jingkang50/openpvsg
공식 코드https://github.com/jinglinglingling/x4d
공식 코드https://github.com/jinyjelly/videomamba
공식 코드https://github.com/jnzs1836/intent-vizor
공식 코드IntentVizor: Towards Generic Query Guided Interactive Video Summarization
https://github.com/joeleelyf/ovo-bench
공식 코드OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
https://github.com/joez17/videoniah
공식 코드Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs
https://github.com/johncaged/vrope
공식 코드VRoPE: Rotary Position Embedding for Video Large Language Models
https://github.com/johnsonsign/mast-pre
공식 코드Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
https://github.com/josephzpng/distime
공식 코드DisTime: Distribution-based Time Representation for Video Large Language Models
https://github.com/joshuaebenezer/lbmfr-public
공식 코드Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates
https://github.com/jqtangust/hawk
공식 코드https://github.com/ju-chen/Efficient-Prompt
공식 코드Prompting Visual-Language Models for Efficient Video Understanding
https://github.com/junjie99/mlvu
공식 코드https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection
공식 코드https://github.com/jyrao/UniSoccer
공식 코드https://github.com/k-zha14/zoom-vqa
공식 코드Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
https://github.com/kahnchana/mvu
공식 코드https://github.com/kalofan/autoscanpathqa
공식 코드Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
https://github.com/kangsankim07/videoicl
공식 코드VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
https://github.com/kapildeshpande/anomaly-detection-in-surveillance-videos
공식 코드Anomaly detection in surveillance videos using transformer based attention model
https://github.com/katou2/cstp
공식 코드Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation
https://github.com/keflanagan/climer
공식 코드Learning Temporal Sentence Grounding From Narrated EgoVideos
https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-
공식 코드Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
https://github.com/keynekassapa13/LAC
공식 코드Self-Supervised Contrastive Learning for Videos using Differentiable Local Alignment
https://github.com/khu-vll/devias
공식 코드DEVIAS: Learning Disentangled Video Representations of Action and Scene
https://github.com/kini5gowda/stories
공식 코드Telling Stories for Common Sense Zero-Shot Action Recognition
https://github.com/kiyoon/channel_sampling
공식 코드Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
https://github.com/kkahatapitiya/langrepo
공식 코드https://github.com/kpeng9510/hopadiff
공식 코드https://github.com/kuangzijian/Improved-Actor-Relation-Graph-based-Group-Activity-Recognition
공식 코드Improved Actor Relation Graph based Group Activity Recognition
https://github.com/kwai-keye/keye
공식 코드https://github.com/lRomul/ball-action-spotting
공식 코드https://github.com/la30/agflow
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.