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/baichuan-inc/Baichuan-Omni-1.5
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
https://github.com/bespontaneous/ffn
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/bespontaneous/mca-pytorch
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/blankWorld/GroupNorm-caffe
https://github.com/boseaslcohort/youtube-8m
https://github.com/bryanyzhu/two-stream-pytorch
https://github.com/caolele/caolele.github.io
Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
https://github.com/changliu816/CV-paper-review
https://github.com/chengyangfu/pytorch-groupnormalization
https://github.com/chriswegmann/drone_steering
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
https://github.com/chunfeng0301/Flow
https://github.com/cmsflash/efficient-attention
https://github.com/code-implementation1/Code1/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code2/tree/main/AdderNGD
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code6/tree/main/Non_local
https://github.com/damien911224/theWorldInSafety
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/daveboat/structured_label_inference
https://github.com/davide-coccomini/TimeSformer-Video-Classification
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/dmonterom/face_recognition_TF2
https://github.com/drv-agwl/ViViT-pytorch
https://github.com/facebookresearch/R2Plus1D
Video Classification with Channel-Separated Convolutional Networks
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/perception_models
Perception Encoder: The best visual embeddings are not at the output of the network
https://github.com/facebookresearch/pytorchvideo
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
Billion-scale semi-supervised learning for image classification
https://github.com/franknb/Self-attention-DCGAN
https://github.com/gerasmark/Reproducing-Unseen-Modality-Interaction
https://github.com/google-research/google-research/tree/master/assemblenet
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
https://github.com/google/youtube-8m
https://github.com/guoxih/busy-quiet-net
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/haofanwang/video-swin-transformer-pytorch
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/iduta/pyconvsegnet
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/innat/VideoSwin
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.