Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2108.13393v1
• Date: Mon, 30 Aug 2021 17:27:43 GMT
• Title: Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
• Authors: Hongjun Chen, Jinbao Wang, Hong Cai Chen, Xiantong Zhen, Feng Zheng, Rongrong Ji, Ling Shao
• Abstract summary: We propose seminar learning, a new learning paradigm for semantic segmentation with click-level supervision. The rationale of seminar learning is to leverage the knowledge from different networks to compensate for insufficient information provided in click-level annotations. Experimental results demonstrate the effectiveness of seminar learning, which achieves the new state-of-the-art performance of 72.51%.
• Score: 149.9226057885554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to their superior trade-off between supervision and annotation cost. In this paper, we propose seminar learning, a new learning paradigm for semantic segmentation with click-level supervision. The fundamental rationale of seminar learning is to leverage the knowledge from different networks to compensate for insufficient information provided in click-level annotations. Mimicking a seminar, our seminar learning involves a teacher-student and a student-student module, where a student can learn from both skillful teachers and other students. The teacher-student module uses a teacher network based on the exponential moving average to guide the training of the student network. In the student-student module, heterogeneous pseudo-labels are proposed to bridge the transfer of knowledge among students to enhance each other's performance. Experimental results demonstrate the effectiveness of seminar learning, which achieves the new state-of-the-art performance of 72.51% (mIOU), surpassing previous methods by a large margin of up to 16.88% on the Pascal VOC 2012 dataset.

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