385 citations · 1.3k across the 38 of their papers we have counts for
10 papers · 1 filter
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Limin Wang, Sheng Guo, Weilin Huang +2
Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. S…
Detecting Text in Natural Image with Connectionist Text Proposal Network
Zhi Tian, Weilin Huang, Tong He +2
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine-scale text p…
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
Limin Wang, Zhe Wang, Yu Qiao +1
Event recognition in still images is an intriguing problem and has potential for real applications. This paper addresses the problem of event recognition by proposing a convolution…
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
Zhe Wang, Limin Wang, Yali Wang +2
Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods…
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Limin Wang, Yuanjun Xiong, Zhe Wang +4
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is…
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
Yuanjun Xiong, Limin Wang, Zhe Wang +7
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segm…