J
Jianming Zhang
Researcher at Adobe Systems
Publications - 134
Citations - 6292
Jianming Zhang is an academic researcher from Adobe Systems. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 26, co-authored 109 publications receiving 4641 citations. Previous affiliations of Jianming Zhang include The Chinese University of Hong Kong & Boston University.
Papers
More filters
Book ChapterDOI
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization
TL;DR: It is shown that the proposed multi-expert restoration scheme significantly improves the robustness of the base tracker, especially in scenarios with frequent occlusions and repetitive appearance variations.
Journal ArticleDOI
Top-Down Neural Attention by Excitation Backprop
TL;DR: A new backpropagation scheme, called Excitation Backprop, is proposed to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process, and the concept of contrastive attention is introduced to make the top- down attention maps more discriminative.
Proceedings ArticleDOI
Saliency Detection: A Boolean Map Approach
Jianming Zhang,Stan Sclaroff +1 more
TL;DR: A novel Boolean Map based Saliency model, based on a Gestalt principle of figure-ground segregation, that consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets.
Proceedings ArticleDOI
Minimum Barrier Salient Object Detection at 80 FPS
TL;DR: A technique based on color whitening is proposed to extend the salient object detection method to leverage the appearance-based backgroundness cue, which further improves the performance, while still being one order of magnitude faster than all the other leading methods.
Book ChapterDOI
Top-Down Neural Attention by Excitation Backprop
TL;DR: A new backpropagation scheme, called Excitation Backprop, is proposed to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process, and the concept of contrastive attention is introduced to make the top- down attention maps more discriminative.