Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

TL;DR

This paper introduces a semantic-aware dual contrastive learning framework for multi-label image classification, effectively capturing complex label relationships and local discriminative features, leading to improved performance over state-of-the-art methods.

Contribution

The paper proposes a novel dual contrastive learning approach combining sample-to-sample and prototype-to-sample contrastive modules for enhanced multi-label image classification.

Findings

Outperforms state-of-the-art methods on five large-scale datasets

Effectively captures intra- and inter-category semantic relationships

Improves discriminative feature learning for multi-label classification

Abstract

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.