https://github.com/facebookresearch/pytorchvideo
공식 코드PyTorchVideo: A Deep Learning Library for Video Understanding
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/facebookresearch/video-long-term-feature-banks
공식 코드https://github.com/fansunqi/akeys
공식 코드https://github.com/farewellthree/BT-Adapter
공식 코드BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
https://github.com/farewellthree/ppllava
공식 코드PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
https://github.com/farewellthree/stan
공식 코드Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding
https://github.com/felixfeng2019/struggle-determination
공식 코드Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videos
https://github.com/fjchange/MIST_VAD
공식 코드MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
https://github.com/fmthoker/severe-benchmark
공식 코드How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
https://github.com/fpv-iplab/differentiable-task-graph-learning
공식 코드https://github.com/fpv-iplab/easg
공식 코드Action Scene Graphs for Long-Form Understanding of Egocentric Videos
https://github.com/freedomintelligence/longllava
공식 코드LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
https://github.com/friedrichor/TUNA
공식 코드TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
https://github.com/fuchenustc/sifa
공식 코드https://github.com/fujiry0/egosurgery
공식 코드EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos
https://github.com/gaotiexinqu/v2p-bench
공식 코드https://github.com/genforce/StyleSV
공식 코드https://github.com/georgegotin/ic2vqa
공식 코드Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics
https://github.com/gfxdisp/asap
공식 코드https://github.com/gls0425/linvt
공식 코드LinVT: Empower Your Image-level Large Language Model to Understand Videos
https://github.com/google-deepmind/neptune
공식 코드Neptune: The Long Orbit to Benchmarking Long Video Understanding
https://github.com/google-research-datasets/i3-video
공식 코드Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/musiq
공식 코드https://github.com/google-research/google-research/tree/master/tiny_video_nets
공식 코드https://github.com/google-research/scenic
공식 코드TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/gorjanradevski/multimodal-distillation
공식 코드https://github.com/gorjanradevski/revisiting-spatial-temporal-layouts
공식 코드Revisiting spatio-temporal layouts for compositional action recognition
https://github.com/griffbr/supervoxel-gerrymandering
공식 코드Video Object Segmentation using Supervoxel-Based Gerrymandering
https://github.com/guanxiongsun/stpn
공식 코드Spatio-temporal Prompting Network for Robust Video Feature Extraction
https://github.com/guanxiongsun/vfe.pytorch
공식 코드Spatio-temporal Prompting Network for Robust Video Feature Extraction
https://github.com/gumpest/sparsevlms
공식 코드SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
https://github.com/gyxxyg/trace
공식 코드TRACE: Temporal Grounding Video LLM via Causal Event Modeling
https://github.com/gzhu-dvl/attackvqa
공식 코드Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
https://github.com/gzhu-dvl/starvqaplus
공식 코드StarVQA+: Co-training Space-Time Attention for Video Quality Assessment
https://github.com/hansxsourse/vrmdiff
공식 코드VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.