Chenwei Deng

TL;DR: Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods, and multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

TL;DR: Compressed domain is adopted for fast ship candidate extraction, DNN is exploited for high-level feature representation and classification, and ELM is used for efficient feature pooling and decision making.

TL;DR: In this letter, an enhanced pixel domain JND model with a new algorithm for CM estimation is proposed, and the proposed one shows its advantages brought by the better EM and TM estimation.

TL;DR: This work exploits temporal information in fine image sequences and solves the spatiotemporal fusion problem with a two-stream convolutional neural network called StfNet with a temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results.

TL;DR: This paper builds an image retargeting quality database, and demonstrates that the metric performance can be further improved, by fusing the descriptors of shape distortion and content information loss.