Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization

TL;DR

This paper introduces an adaptive method for weakly supervised object localization that refines proposal sets into pseudo object labels, significantly improving detection accuracy on standard datasets.

Contribution

It proposes a novel adaptive framework combining MIL, proposal re-weighting, and pseudo-label generation to enhance weakly supervised object detection.

Findings

Achieves state-of-the-art results on PASCAL VOC datasets.

Effectively eliminates noisy proposals and generates accurate pseudo labels.

Demonstrates significant performance improvements over existing methods.

Abstract

In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The proposed approach is built on the observation that the proposal set from the training dataset is a collection of background, object parts, and objects. Several strategies are taken to adaptively eliminate the noisy proposals and generate pseudo object-level annotations for the weakly labeled dataset. A multiple instance learning (MIL) algorithm enhanced by mask-out strategy is adopted to collect the class-specific object proposals, which are then utilized to adapt a pre-trained classification network to a detection network. In addition, the detection results from the detection network are re-weighted by jointly considering the detection scores and the overlap ratio of proposals in a proposal subset optimization framework. The optimal proposals work as object-level labels that enable a pseudo-strongly supervised dataset for training the detection network. Consequently, we establish a fully adaptive detection network. Extensive evaluations on the PASCAL VOC 2007 and 2012 datasets demonstrate a significant improvement compared with the state-of-the-art methods.