Collaborative Learning of Semantic-Aware Feature Learning and Label Recovery for Multi-Label Image Recognition with Incomplete Labels
This addresses the problem of handling missing labels in multi-label image recognition for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled multi-label image recognition with incomplete labels by proposing a collaborative learning framework that unifies semantic-aware feature learning and label recovery, achieving state-of-the-art performance on datasets like MS-COCO, VOC2007, and NUS-WIDE.
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.