no code implementations • IEEE Transactions on Cybernetics 2023 • Lisha Cui, Pei Lv, Xiaoheng Jiang, Zhimin Gao, Bing Zhou, Luming Zhang, Ling Shao, Mingliang Xu
State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects.
Ranked #1 on Traffic Sign Detection on TT100K
no code implementations • 23 Sep 2020 • Ping Li, Qinghao Ye, Luming Zhang, Li Yuan, Xianghua Xu, Ling Shao
In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames.
no code implementations • 22 Aug 2018 • Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen
Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics.
no code implementations • 15 Nov 2016 • Ping Li, Jun Yu, Meng Wang, Luming Zhang, Deng Cai, Xuelong. Li
To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.
no code implementations • 7 Nov 2016 • Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum
As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life.
no code implementations • 25 May 2016 • Yanxiang Chen, Yuxing Hu, Luming Zhang, Ping Li, Chao Zhang
To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting.
no code implementations • 6 Oct 2014 • Yuxin Hu, Luming Zhang
Aerial image categorization plays an indispensable role in remote sensing and artificial intelligence.
no code implementations • CVPR 2013 • Luming Zhang, Mingli Song, Zicheng Liu, Xiao Liu, Jiajun Bu, Chun Chen
Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.
no code implementations • CVPR 2013 • Xiao Liu, Mingli Song, DaCheng Tao, Zicheng Liu, Luming Zhang, Chun Chen, Jiajun Bu
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.