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Bin Wang 0021

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2 papers

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ECAI Conference 2024 Conference Paper

Boundary-Enhanced Instance Segmentation

Fangyuan Zhang
Tianxiang Pan
Yu-Wing Tai
Bin Wang 0021

Despite significant progress in instance segmentation, recent solutions still fall short of boundary accuracy especially for overlapping instances of the same category. In this paper, we propose a novel boundary-enhanced instance segmentation (BEIS) framework that explicitly models the feature relationships across object boundaries for high-quality instance segmentation. Specifically, BEIS generates boundary-enhanced features using both intra-mask and cross-image boundary discrimination learning. The intra-mask boundary discrimination learning (IBDL) employs pixel-level discrimination learning to disentangle pixel representations along boundaries. The cross-image boundary discrimination learning (CBDL) learns a boundary-aware feature bank from training data to further boost the performance. Thus, CBDL can take advantage of boundary relations across images to enhance the quality of segmented boundaries. To focus on hard-to-segment boundaries, we propose an adaptive sampling strategy to automatically construct discriminative pairs in regions with high possibilities of confusion. Extensive experiments show BEIS outperforms on various datasets.

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ICML Conference 2021 Conference Paper

Unsupervised Co-part Segmentation through Assembly

Qingzhe Gao
Bin Wang 0021
Libin Liu 0002
Baoquan Chen

Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For the training stage, we leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful object parts. More importantly, we introduce a dual procedure of part-assembly to form a closed loop with part-segmentation, enabling an effective self-supervision. We demonstrate the effectiveness of our approach with a host of extensive experiments, ranging from human bodies, hands, quadruped, and robot arms. We show that our approach can achieve meaningful and compact part segmentation, outperforming state-of-the-art approaches on diverse benchmarks.

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