Deep patch learning for weakly supervised object classification and discovery

Peng Tang, Xinggang Wang, Zilong Huang, Xiang Bai, Wenyu Liu

Abstract: Highlights•We propose to integrate different patch-based object classification stages into a weakly supervised deep CNN framework.•We integrate the two MIL constraints into the loss of our deep CNN framework for object discovery.•We embed object classification and discovery into a multi-task CNN, and demonstrate they are complementary.•Our method DPL learns patch features end-to-end, and is more effective and efficient than previous patch-based CNNs.•DPL obtains state-of-the-art results on classification and competitive results on discovery, with fast testing speed.

External IDs:dblp:journals/pr/TangWHBL17