Generalized Class Discovery in Instance Segmentation

• URL: http://arxiv.org/abs/2502.08149v1
• Date: Wed, 12 Feb 2025 06:26:05 GMT
• Title: Generalized Class Discovery in Instance Segmentation
• Authors: Cuong Manh Hoang, Yeejin Lee, Byeongkeun Kang,
• Abstract summary: We propose an instance-wise temperature assignment (ITA) method for contrastive learning and class-wise reliability criteria for pseudo-labels. We evaluate our proposed method by conducting experiments on two settings: COCO$_half$ + LVIS and LVIS + Visual Genome.
• Score: 7.400926717561454
• License:
• Abstract: This work addresses the task of generalized class discovery (GCD) in instance segmentation. The goal is to discover novel classes and obtain a model capable of segmenting instances of both known and novel categories, given labeled and unlabeled data. Since the real world contains numerous objects with long-tailed distributions, the instance distribution for each class is inherently imbalanced. To address the imbalanced distributions, we propose an instance-wise temperature assignment (ITA) method for contrastive learning and class-wise reliability criteria for pseudo-labels. The ITA method relaxes instance discrimination for samples belonging to head classes to enhance GCD. The reliability criteria are to avoid excluding most pseudo-labels for tail classes when training an instance segmentation network using pseudo-labels from GCD. Additionally, we propose dynamically adjusting the criteria to leverage diverse samples in the early stages while relying only on reliable pseudo-labels in the later stages. We also introduce an efficient soft attention module to encode object-specific representations for GCD. Finally, we evaluate our proposed method by conducting experiments on two settings: COCO$_{half}$ + LVIS and LVIS + Visual Genome. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods.

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