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Contrastive Proposal Extension with Sequential Network for Weakly Supervised Object Detection

2021-10-14 16:31:57
Pei Lv, Suqi Hu, Tianran Hao, Haohan Ji, Lisha Cui, Haoyi Fan, Mingliang Xu, Changsheng Xu

Abstract

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. %\textcolor{red}{with temporal network}. 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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.07511

PDF

https://arxiv.org/pdf/2110.07511.pdf


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