Online progressive instance-balanced sampling for weakly supervised object detection

TL;DR

This paper introduces an online progressive sampling method for weakly supervised object detection that effectively balances positive and negative instances during training, improving performance without extra network complexity.

Contribution

The paper proposes a novel OPIS algorithm with PIB and PIR modules to address negative instance dominance in WSOD, enhancing detection accuracy.

Findings

Significant performance improvement on PASCAL VOC datasets

Comparable to state-of-the-art methods

No extra network parameters required

Abstract

Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm.