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/jayChung0302/videomix
VideoMix: Rethinking Data Augmentation for Video Classification
https://github.com/jeffreyhuang1/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/jeffreyyihuang/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/jerrywn121/TianChi_AIEarth
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jordiae/DeepLearning-MAI
https://github.com/jpviguerasguillen/feedback-non-local-attention-fnla
https://github.com/jpviguerasguillen/feedback-non-local-attention_fNLA
https://github.com/jssprz/video_captioning_datasets
On the effectiveness of task granularity for transfer learning
https://github.com/jwwoody/mlb-deeplearning
https://github.com/jwwoody/mlb-youtube
https://github.com/keras-team/keras-io/blob/master/examples/vision/vivit.py
https://github.com/kingcong/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/kkahatapitiya/X3D-Multigrid
https://github.com/kusumikakd/EEGLearn
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/kylemath/DeepEEG
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/kylemin/S3D
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/leaderj1001/Billion-scale-semi-supervised-learning
Billion-scale semi-supervised learning for image classification
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/uniformer
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/libffcv/ffcv-imagenet
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
https://github.com/linrongc/youtube-8m
https://github.com/liu824/representation-flow-for-action-recognition-paddlepaddle
https://github.com/liuruiyang98/Jittor-MLP
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
https://github.com/lucidrains/TimeSformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/lucidrains/vit-pytorch
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
https://github.com/luigisigillo/gwit
https://github.com/m-bain/video-transformers
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mamonraab/Real-Time-Violence-Detection-in-Video-
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/md-mohaiminul/objectstatechange
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mezo11/2
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
https://github.com/mfayk/cu_icar_image_captioning
https://github.com/mindspore-ai/models/tree/master/research/cv/AttentionCluster
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.