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/sczwangxiao/video-flexreduc
공식 코드AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding
https://github.com/sczwangxiao/video-retake
공식 코드ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding
https://github.com/sdqesdataset/sdqes_generation
공식 코드https://github.com/seanzhuh/awesome-open-vocabulary-detection-and-segmentation
공식 코드A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
https://github.com/shankhanil006/ssl-vqa
공식 코드Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
https://github.com/shankhanil006/vision
공식 코드https://github.com/shengyangsun/MSBT
공식 코드Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection
https://github.com/shengyu-feng/dsg-detr
공식 코드Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
https://github.com/shi-labs/slow-fast-video-multimodal-llm
공식 코드Slow-Fast Architecture for Video Multi-Modal Large Language Models
https://github.com/shinkyo0513/Video-Visual-Explanations
공식 코드Towards Visually Explaining Video Understanding Networks with Perturbation
https://github.com/showlab/movieseq
공식 코드https://github.com/showlab/show-1
공식 코드Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
https://github.com/showlab/vlog
공식 코드VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary
https://github.com/shuyansy/vidtext
공식 코드VidText: Towards Comprehensive Evaluation for Video Text Understanding
https://github.com/shvdiwnkozbw/Self-supervised-Video-Concept
공식 코드Static and Dynamic Concepts for Self-supervised Video Representation Learning
https://github.com/shvdiwnkozbw/video-representation-via-multi-level-optimization
공식 코드Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
https://github.com/sissuire/sama
공식 코드Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment
https://github.com/sitonggong/avs-mamba
공식 코드AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
https://github.com/sivandoveh/iploc
공식 코드Teaching VLMs to Localize Specific Objects from In-context Examples
https://github.com/sixcorepeach/mcam
공식 코드MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
https://github.com/smile-data/smile
공식 코드SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models
https://github.com/sming256/OpenTAD
공식 코드Harnessing Temporal Causality for Advanced Temporal Action Detection
https://github.com/sming256/bolt
공식 코드BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
https://github.com/sming256/etad
공식 코드https://github.com/sming256/timeloc
공식 코드TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
https://github.com/soccernet/sn-calibration
공식 코드https://github.com/soccernet/sn-grounding
공식 코드https://github.com/soccernet/sn-reid
공식 코드https://github.com/soccernet/sn-spotting
공식 코드https://github.com/soccernet/sn-tracking
공식 코드https://github.com/solicucu/d3g
공식 코드D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
https://github.com/ssarfraz/FINCH-CLustering
공식 코드Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
https://github.com/steven-ccq/vilamp
공식 코드Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
https://github.com/sts-vlcc/sts-vlcc
공식 코드https://github.com/sunilhoho/VideoMS
공식 코드EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.