Weak to Strong Detector Learning for Simultaneous Classification and Localization

Abstract

This paper aims at learning discriminative part detectors with only image-level labels. To this end, we need to develop effective technologies for both pattern mining and detection learning. Different from previous methods, which train part detectors in one step, we divide the detector learning process into two stages and formulate it as a weak to strong learning framework. In particular, we first learn exemplar detectors from the unaligned patterns and perform a detector-based spectral clustering to produce weak detectors that are only responsible for a few discriminative patterns. In this way, the weak detectors are able to offer right initial patterns for strong detector learning. Second, we learn strong detectors with patterns discovered from the weak detectors, which we formulate as a confidence-loss sparse multiple instance learning (cls-MIL) task. The cls-MIL considers the diversity of positive samples while avoiding drifting away from the well localized ones by assigning a confidence value to each positive sample. The responses of the learned detectors produce an effective mid-level image representation for both image classification and object localization. Experiments conducted on benchmark data sets well demonstrate the superiority of our method over existing approaches.