https://github.com/MCG-NJU/MultiSports
공식 코드MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MCG-NJU/RTD-Action
공식 코드Relaxed Transformer Decoders for Direct Action Proposal Generation
https://github.com/MCG-NJU/SportsMOT
공식 코드SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
https://github.com/MCG-NJU/TDN
공식 코드TDN: Temporal Difference Networks for Efficient Action Recognition
https://github.com/MCG-NJU/VideoMAE
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MCG-NJU/VideoMAE-Action-Detection
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MKFMIKU/VIDM
공식 코드https://github.com/MKLab-ITI/visil
공식 코드ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning
https://github.com/MMKE-Bench-ICLR/MMKE-Bench
공식 코드MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
https://github.com/MSiam/AdaptiveMaskedProxies
공식 코드https://github.com/MSiam/motion_adaptation
공식 코드https://github.com/MTLab/MorphMLP
공식 코드MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
https://github.com/Maddy12/ActionRecognitionRobustnessEval
공식 코드Large-scale Robustness Analysis of Video Action Recognition Models
https://github.com/Maddy12/MultiModalVideoRobustness/tree/master/code
공식 코드Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
https://github.com/MadryLab/cifar10_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MadryLab/mnist_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Maghoumi/DeepGRU
공식 코드https://github.com/Manu21JC/Dormant
공식 코드Dormant: Defending against Pose-driven Human Image Animation
https://github.com/MarcBS/TMA
공식 코드Egocentric Video Description based on Temporally-Linked Sequences
https://github.com/MarcusOlivecrona/REINVENT
공식 코드Molecular De Novo Design through Deep Reinforcement Learning
https://github.com/MasterBin-IIAU/AlphaRefine
공식 코드Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
https://github.com/MasterBin-IIAU/UNINEXT
공식 코드Universal Instance Perception as Object Discovery and Retrieval
https://github.com/MedAI-Clemson/pda_detection
공식 코드On the Relevance of Temporal Features for Medical Ultrasound Video Recognition
https://github.com/MedChaabane/DEFT
공식 코드https://github.com/Media-Smart/vedatad
공식 코드https://github.com/MegviiDetection/video_analyst
공식 코드https://github.com/MehwishG/T3D-CNN
공식 코드Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
https://github.com/Meixu-Chen/MOVI-Codec
공식 코드https://github.com/MenghaoGuo/Awesome-Vision-Attentions
공식 코드https://github.com/MertCokelek/SalViT360
공식 코드Spherical Vision Transformer for 360-degree Video Saliency Prediction
https://github.com/MichiganCOG/M-PACT
공식 코드M-PACT: An Open Source Platform for Repeatable Activity Classification Research
https://github.com/MichiganCOG/Surgical_Hands_RELEASE
공식 코드Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
https://github.com/MichiganCOG/ViP
공식 코드https://github.com/MichiganNLP/video-fill-in-the-blank
공식 코드FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
https://github.com/Microsoft/EdgeML
공식 코드FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.