Soft Proposal Networks for Weakly Supervised Object Localization

Understand

Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.

• Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules.
• In this paper, to the best of our knowledge, we for the first time integrate weakly supervised object proposal into convolutional neural networks (CNNs) in an end-to-end learning manner.
• We design a network component, Soft Proposal (SP), to be plugged into any standard convolutional architecture to introduce the nearly cost-free object proposal, orders of magnitude faster than state-of-the-art methods.