TY - JOUR AU - Zhang, Xiaopeng AU - Yang, Yang AU - Feng, Jiashi PY - 2019/07/17 Y2 - 2024/03/29 TI - Learning to Localize Objects with Noisy Labeled Instances JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v33i01.33019219 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4957 SP - 9219-9226 AB - <p>This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We model the missing object locations as latent variables, and contribute a novel self-directed optimization strategy to infer them. With the strategy, our developed Self-Directed Localization Network (SD-LocNet) is able to localize object instance whose initial location is noisy. The self-directed inference hinges on an adaptive sampling method to identify reliable object instance via measuring its localization stability score. In this way, the resulted model is robust to noisy initialized object locations which we find is important in WSOL. Furthermore, we introduce a reliability induced prior propagation strategy to transfer object priors of the reliable instances to those unreliable ones by promoting their feature similarity, which effectively refines the unreliable object instances for better localization. The proposed SD-LocNet achieves 70.9% Cor-Loc and 51.3% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.</p> ER -