C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

TL;DR

This paper introduces C-MIL, a continuation optimization approach for weakly supervised object detection that improves localization accuracy by systematically addressing non-convex loss functions in MIL.

Contribution

The paper proposes a continuation optimization method integrated into MIL, which enhances object localization by preventing premature convergence to local minima.

Findings

C-MIL outperforms previous methods on PASCAL VOC datasets.

It achieves significant improvements in object detection accuracy.

The approach effectively discovers full object extents during training.

Abstract

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to get stuck into local minima (falsely localize object parts) while missing full object extent during training. In this paper, we introduce a continuation optimization method into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way. We partition instances into spatially related and class related subsets, and approximate the original loss function with a series of smoothed loss functions defined within the subsets. Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions (SSERs) which indicate full object extent. On the PASCAL VOC 2007 and 2012 datasets, C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins.