Weakly supervised object localization with soft guidance and channel erasing for auto labelling in autonomous driving systems. (January 2023) - Xie, Xinyan; Li, Yijiang; Gao, Ying; Wu, Chaojie; Gao, Ping; Song, Binjie; Wang, Wei; Lu, Yiqin
Abstract: Automated driving systems (ADSs) conceive an efficient and safe way of driving. The safety of ADSs depends on a precise object detector that needs to be upgraded continuously facing various environments. Massive annotations are required to utilize collected images of surroundings through vehicles and accommodate new environments. Auto labelling is one approach to alleviate such dilemma. To this end, we propose a novel Weakly Supervised Object Localization (WSOL) method which can localize objects precisely without detection annotations. This paper proposed Soft Guidance Module (SGM), Channel Erasing Module (CEM) and incorporate them into a multi-flow framework allowing the two mutually beneficial. Finally, experiments and visualizations are performed to evaluate our method on Stanford Cars, ILSVRC 2016 and CUB-200-2011 datasets. Highlights: WSOL is used to generate object annotation for upgrading automated driving systems. Soft Guidance Module detects more complete object including the edge adaptively. Channel Erasing Module promotes networks to focus on more object-related channels. Multi-flow structure combines two erasing modules without additional parameters.