arXiv:2103.15323
Classifying Video based on Automatic Content Detection Overview
원문 초록
Video classification and analysis is always a popular and challenging field in computer vision. It is more than just simple image classification due to the correlation with respect to the semantic contents of subsequent frames brings difficulties for video analysis. In this literature review, we summarized some state-of-the-art methods for multi-label video classification. Our goal is first to experimentally research the current widely used architectures, and then to develop a method to deal with the sequential data of frames and perform multi-label classification based on automatic content detection of video.
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FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
LLSF - Learning Label Specific Features for Multi-Label Classifcation
MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
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