Video Classification
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Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Aprilkaka/nonlocal_mindspore
https://github.com/Asthestarsfalll/UniFormer-MegEngine
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/BB-Repos/BBaction
Video Classification with Channel-Separated Convolutional Networks
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
https://github.com/ChetanTayal138/HAR-Web
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/Chianugoogidi/X3D-tf
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/Cloud-Computing-IoT/Cloud-Enabled-Smart-Speaker
https://github.com/Cloud-Computing-IoT/speakEasy
https://github.com/DeepakRaya/Cognitive_classification_RNN_EEG_images
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/EquipoVandV/mmactionVandV
https://github.com/HsinYingLee/OPN
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Jarvis73/Group_Normalization
https://github.com/JiaPeng1234/MRI-Segmentation-Transformer
https://github.com/JunweiLiang/aicity_action
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/KSonPham/ViVit-a-Pytorch-implementation
https://github.com/KevinPang180/3D-ResNets-PaddlePaddle
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/KiUngSong/Vision
https://github.com/LRacoci/permutation-graphml
https://github.com/LeonShangguan/CSC449_Project
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report
https://github.com/LukasHedegaard/co3d
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/MIT-HAN-LAB/temporal-shift-module
https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local
https://github.com/MS-Mind/MS-Code-08/tree/main/Non_local
https://github.com/MS-P3/code7/tree/main/vivit
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/Mind23-2/MindCode-62
Video Classification with Channel-Separated Convolutional Networks
https://github.com/Mind23-2/MindCode-68
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/MindCode-4/code-12/tree/main/group-normalization
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindCode-4/code-5/tree/main/vit_mae
https://github.com/MindCode-4/code-7/tree/main/group-normalization
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d
Video Classification with Channel-Separated Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.