Zhi Zhang

TL;DR: This article examined a collection of such refinements and empirically evaluated their impact on the final model accuracy through ablation study, and showed that by combining these refinements together, they are able to improve various CNN models significantly.

TL;DR: A simple and modular Split-Attention block that enables attention across feature-map groups ResNet-style is presented that preserves the overall ResNet structure to be used in downstream tasks straightforwardly without introducing additional computational costs.

TL;DR: This paper examines a collection of training procedure refinements and empirically evaluates their impact on the final model accuracy through ablation study, and shows that by combining these refinements together, they are able to improve various CNN models significantly.

TL;DR: This paper develops a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking that exploits the history of locations as well as the distinctive visual features learned by the deep neural networks to concatenate high-level visual features produced by Convolutional networks with region information.

TL;DR: The Hard X-ray Modulation Telescope (HXMT) as mentioned in this paper is a wide-band slat-collimator-based satellite with the capability of all-sky monitoring in 0.2-3 MeV.