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Video Classification with Channel-Separated Convolutional Networks
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Video Classification with Channel-Separated Convolutional Networks
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
Beyond Short Snippets: Deep Networks for Video Classification
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
Adversarial Perturbations Against Real-Time Video Classification Systems
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
Progressive Video Summarization via Multimodal Self-supervised Learning
Billion-scale semi-supervised learning for image classification
Is Space-Time Attention All You Need for Video Understanding?
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Two-Stream Convolutional Networks for Action Recognition in Videos
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Is Space-Time Attention All You Need for Video Understanding?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.