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We address the challenge of learning good video representations by explicitly modeling the relationship between visual concepts in time space. We propose a novel Temporal Preserving Recurrent Neural Network (TPRNN) that extracts and encodes visual dynamics with frame-level features as input. The proposed network architecture captures temporal dynamics by keeping track of the ordinal relationship of...
This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image...
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