  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.
