Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping

TL;DR

This paper introduces a novel weakly-supervised concealed object segmentation method leveraging SAM-based pseudo labels and multi-scale feature grouping, achieving state-of-the-art results despite limited annotations.

Contribution

It proposes a multi-scale feature grouping module and strategies to improve pseudo label quality using SAM, addressing intrinsic similarity and weak supervision challenges in WSCOS.

Findings

Achieves state-of-the-art performance on WSCOS tasks.

Effectively groups features at multiple scales for better segmentation.

Utilizes SAM prompts and strategies to enhance pseudo label reliability.

Abstract

Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment objects well blended with surrounding environments using sparsely-annotated data for model training. It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning. In this paper, we propose a new WSCOS method to address these two challenges. To tackle the intrinsic similarity challenge, we design a multi-scale feature grouping module that first groups features at different granularities and then aggregates these grouping results. By grouping similar features together, it encourages segmentation coherence, helping obtain complete segmentation results for both single and multiple-object images. For the weak supervision challenge, we utilize the recently-proposed vision foundation model, Segment Anything Model (SAM), and use the provided sparse annotations as prompts to generate segmentation masks, which are used to train the model. To alleviate the impact of low-quality segmentation masks, we further propose a series of strategies, including multi-augmentation result ensemble, entropy-based pixel-level weighting, and entropy-based image-level selection. These strategies help provide more reliable supervision to train the segmentation model. We verify the effectiveness of our method on various WSCOS tasks, and experiments demonstrate that our method achieves state-of-the-art performance on these tasks.