https://github.com/JeffCHEN2017/WSSTG
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Papers with Code 아카이브
Video Object Tracking
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Video Object Detection aims to detect targets in videos using both spatial and temporal information. It's usually deeply integrated with tasks such as Object Detection and Object Tracking.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KingGugu/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/LdDl/rust-road-traffic
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/LukasHedegaard/co3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/MarkoLewis-Projects/Sign_language_detection
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Mind23-2/MindCode-129
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Mind23-2/MindCode-74
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleDetection
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleYOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Paulpauladq/HighSpeedFaceTracker
https://github.com/ShobhitMaheshwari/sign-language1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/SonginCV/GMPHD_MAF
https://github.com/SonginCV/GMPHD_SAF
https://github.com/SonginCV/MAF_HDA
https://github.com/StanfordVL/RubiksNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Superlee506/Correlation_Filtering_Tracking_Lib
https://github.com/WavesUR/embedded_TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/WongKinYiu/YOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/ZC119/richardson_tracking
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ahsaniqbal/Kinetics-FeatureExtractor
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/aim3-ruc/youmakeup_challenge2022
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/anonymous-p/Flickering_Adversarial_Video
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/bespontaneous/ffn
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bespontaneous/mca-pytorch
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bingfengyan/visam
Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
https://github.com/code-implementation1/Code9/tree/main/ocean_model
https://github.com/cyx2016/siamrpn_mindspore
https://github.com/daniansan/i3d_mindspore
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dnozza/profanity-obfuscation
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.