Semantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification
This work addresses multi-label image classification for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackled the problem of multi-label image classification by addressing limitations in learning discriminative semantic-aware features and fine-grained alignment between visual and label representations, proposing SCT which achieved superior performance on VOC2007 and MS-COCO benchmarks.
Multi-label image classification is a critical task in machine learning that aims to accurately assign multiple labels to a single image. While existing methods often utilize attention mechanisms or graph convolutional networks to model visual representations, their performance is still constrained by two critical limitations: the inability to learn discriminative semantic-aware features, and the lack of fine-grained alignment between visual representations and label embeddings. To tackle these issues in a unified framework, this paper proposes a novel approach named Semantic-aware representation learning via Conditional Transport for Multi-Label Image Classification (SCT). The proposed method introduces a semantic-related feature learning module that extracts discriminative label-specific features by emphasizing semantic relevance and interaction, along with a conditional transport-based alignment mechanism that enables precise visual-semantic alignment. Extensive experiments on two widely-used benchmark datasets, VOC2007 and MS-COCO, validate the effectiveness of SCT and demonstrate its superior performance compared to existing state-of-the-art methods.