HMFS: Hybrid Masking for Few-Shot Segmentation

• URL: http://arxiv.org/abs/2203.12826v1
• Date: Thu, 24 Mar 2022 03:07:20 GMT
• Title: HMFS: Hybrid Masking for Few-Shot Segmentation
• Authors: Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Muhammad Haris Khan, and Mubbasir Kapadia
• Abstract summary: We develop a simple, effective, and efficient approach to enhance feature masking (FM) We compensate for the loss of fine-grained spatial details in FM technique by investigating and leveraging a complementary basic input masking method. Experimental results on three publicly available benchmarks reveal that HMFS outperforms the current state-of-the-art methods by visible margins.
• Score: 27.49000348046462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. Several recent methods resort to a feature masking (FM) technique, introduced by [1], to discard irrelevant feature activations to facilitate reliable segmentation mask prediction. A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects. In this paper, we develop a simple, effective, and efficient approach to enhance feature masking (FM). We dub the enhanced FM as hybrid masking (HM). Specifically, we compensate for the loss of fine-grained spatial details in FM technique by investigating and leveraging a complementary basic input masking method [2]. To validate the effectiveness of HM, we instantiate it into a strong baseline [3], and coin the resulting framework as HMFS. Experimental results on three publicly available benchmarks reveal that HMFS outperforms the current state-of-the-art methods by visible margins.

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