  Weakly supervised object detection (WSOD) has attracted more and more
attention since it only uses image-level labels and can save huge annotation
costs. Most of the WSOD methods use Multiple Instance Learning (MIL) as their
basic framework, which regard it as an instance classification problem.
However, these methods based on MIL tends to converge only on the most
discriminate regions of different instances, rather than their corresponding
complete regions, that is, insufficient integrity. Inspired by the habit of
observing things by the human, we propose a new method by comparing the initial
proposals and the extension ones to optimize those initial proposals.
Specifically, we propose one new strategy for WSOD by involving contrastive
proposal extension (CPE), which consists of multiple directional contrastive
proposal extensions (D-CPE), and each D-CPE contains encoders based on LSTM
network and corresponding decoders. Firstly, the boundary of initial proposals
in MIL is extended to different positions according to well-designed sequential
order. Then, CPE compares the extended proposal and the initial proposal by
extracting the feature semantics of them using the encoders, and calculates the
integrity of the initial proposal to optimize the score of the initial
proposal. These contrastive contextual semantics will guide the basic WSOD to
suppress bad proposals and improve the scores of good ones. In addition, a
simple two-stream network is designed as the decoder to constrain the temporal
coding of LSTM and improve the performance of WSOD further. Experiments on
PASCAL VOC 2007, VOC 2012 and MS-COCO datasets show that our method has
achieved the state-of-the-art results.
