6 citations · 24 across the 7 of their papers we have counts for
7 papers · 1 filter
Deep Negative Correlation Classification
Le Zhang, Qibin Hou, Yun Liu +4
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm. Existing deep ensemble methods usually naively train many dif…
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng +3
Triplet loss has been widely employed in a wide range of computer vision tasks, including local descriptor learning. The effectiveness of the triplet loss heavily relies on the tri…
Robust Regression via Deep Negative Correlation Learning
Le Zhang, Zenglin Shi, Ming-Ming Cheng +5
Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning…
Salient Object Detection via High-to-Low Hierarchical Context Aggregation
Yun Liu, Yu Qiu, Le Zhang +3
Recent progress on salient object detection mainly aims at exploiting how to effectively integrate convolutional side-output features in convolutional neural networks (CNN). Based…
MatchBench: An Evaluation of Feature Matchers
JiaWang Bian, Ruihan Yang, Yun Liu +4
Feature matching is one of the most fundamental and active research areas in computer vision. A comprehensive evaluation of feature matchers is necessary, since it would advance bo…
Learning Pixel-wise Labeling from the Internet without Human Interaction
Yun Liu, Yujun Shi, JiaWang Bian +3
Deep learning stands at the forefront in many computer vision tasks. However, deep neural networks are usually data-hungry and require a huge amount of well-annotated training samp…
Image Matching: An Application-oriented Benchmark
JiaWang Bian, Le Zhang, Yun Liu +3
Image matching approaches have been widely used in computer vision applications in which the image-level matching performance of matchers is critical. However, it has not been well…