# Semantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification Source: [Semantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification]() Collection index: [llms.txt]() Data updated: 2025-11-05T05:02:10.101Z Authors: Ren-Dong Xie, Zhi-Fen He, Bo Li, Bin Liu, Jin-Yan Hu Published: 2025-07-20T11:15:24.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()