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

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.

Accept by CVPR2019

References in corpus (5)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
• Weakly-supervised Discovery of Visual Pattern Configurations
• On learning to localize objects with minimal supervision

Cited by in corpus (5)

• A Survey of Deep Learning-based Object Detection
• Attention on Attention for Image Captioning
• Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
• Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
• Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection