Collaborative Learning of Semantic-Aware Feature Learning and Label Recovery for Multi-Label Image Recognition with Incomplete Labels

• URL: http://arxiv.org/abs/2510.10055v1
• Date: Sat, 11 Oct 2025 06:43:43 GMT
• Title: Collaborative Learning of Semantic-Aware Feature Learning and Label Recovery for Multi-Label Image Recognition with Incomplete Labels
• Authors: Zhi-Fen He, Ren-Dong Xie, Bo Li, Bin Liu, Jin-Yan Hu,
• Abstract summary: We propose a novel Collaborative Learning of Semantic-aware feature learning and Label recovery method.<n>We show that CLSL outperforms the state-of-the-art multi-label image recognition methods with incomplete labels.
• Score: 8.864897133482907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label image recognition with incomplete labels is a critical learning task and has emerged as a focal topic in computer vision. However, this task is confronted with two core challenges: semantic-aware feature learning and missing label recovery. In this paper, we propose a novel Collaborative Learning of Semantic-aware feature learning and Label recovery (CLSL) method for multi-label image recognition with incomplete labels, which unifies the two aforementioned challenges into a unified learning framework. More specifically, we design a semantic-related feature learning module to learn robust semantic-related features by discovering semantic information and label correlations. Then, a semantic-guided feature enhancement module is proposed to generate high-quality discriminative semantic-aware features by effectively aligning visual and semantic feature spaces. Finally, we introduce a collaborative learning framework that integrates semantic-aware feature learning and label recovery, which can not only dynamically enhance the discriminability of semantic-aware features but also adaptively infer and recover missing labels, forming a mutually reinforced loop between the two processes. Extensive experiments on three widely used public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that CLSL outperforms the state-of-the-art multi-label image recognition methods with incomplete labels.

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