https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bmn
공식 코드BMN: Boundary-Matching Network for Temporal Action Proposal Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PallottaEnrico/SyncVP
공식 코드SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction
https://github.com/Paranioar/UniPT
공식 코드UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
https://github.com/ParitoshParmar/Fitness-AQA
공식 코드Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/MTL-AQA
공식 코드What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/ParitoshParmar/Piano-Skills-Assessment
공식 코드https://github.com/ParitoshParmar/Win-Fail-Action-Recognition
공식 코드https://github.com/Paulkie99/imm-jhse
공식 코드One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation
https://github.com/PeihaoChen/RSPNet
공식 코드RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
https://github.com/PeisenZhao/Bottom-Up-TAL-with-MR
공식 코드Bottom-Up Temporal Action Localization with Mutual Regularization
https://github.com/PeizeSun/TransTrack
공식 코드https://github.com/PeizhuoLi/neural-blend-shapes
공식 코드https://github.com/PetarV-/GAT
공식 코드https://github.com/PhyllisH/NMMP
공식 코드Collaborative Motion Prediction via Neural Motion Message Passing
https://github.com/Pilhyeon/BAM-DETR
공식 코드BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos
https://github.com/Pilhyeon/BaSNet-pytorch
공식 코드Background Suppression Network for Weakly-supervised Temporal Action Localization
https://github.com/Pilhyeon/Learning-Action-Completeness-from-Points
공식 코드Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization
https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling
공식 코드Weakly-supervised Temporal Action Localization by Uncertainty Modeling
https://github.com/PingYu-iris/SA-GCN
공식 코드https://github.com/PinzhengWang322/Reveal_Attention_Drift
공식 코드Revealing and Mitigating Over-Attention in Knowledge Editing
https://github.com/PolyU-ChenLab/ETBench
공식 코드E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
https://github.com/PolymathicAI/lola
공식 코드Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
https://github.com/Qualcomm-AI-research/FrameExit
공식 코드FrameExit: Conditional Early Exiting for Efficient Video Recognition
https://github.com/QualityAssessment/DOVER
공식 코드https://github.com/QualityAssessment/DisCoVQA
공식 코드DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
https://github.com/QualityAssessment/FAST-VQA-and-FasterVQA
공식 코드Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
https://github.com/QuanjianSong/LightMotion
공식 코드A Light and Tuning-free Method for Simulating Camera Motion in Video Generation
https://github.com/QuanjianSong/UniVST
공식 코드UniVST: A Unified Framework for Training-free Localized Video Style Transfer
https://github.com/RAISELab-atUVA/Projected-Diffusion
공식 코드https://github.com/RQ-Wu/LAMP
공식 코드LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
https://github.com/RS2002/CrossFi
공식 코드CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
https://github.com/RS2002/KNN-MMD
공식 코드KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
https://github.com/RalphMao/PatchNet
공식 코드PatchNet -- Short-range Template Matching for Efficient Video Processing
https://github.com/Rbrq03/ClassDiffusion
공식 코드ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
https://github.com/RedShift51/fast-latent-decoders
공식 코드Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.