.png){kind=icon}
.png){kind=icon}
Return
Multi-Label Image Classification via Contrastive Co-Occurrence Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2026.3700934.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label image classification is an essential task in computer vision that aims to identify multiple objects in images. Recently, there has been growing research interest in modeling the relationships between labels to enhance label representation learning. An intuitive approach is to train a network to estimate label co-occurrence probabilities in a supervised manner, which are then leveraged to guide the interactions between label representations. However, the extreme sparsity of label co-occurrence signals poses substantial challenges. To address this issue, we commence by examining the potential interaction behaviors between label representations under the guidance of ground-truth label co-occurrence signals. Inspired by our findings, a novel contrastive learning mechanism is crafted to mimic and enhance such behaviors, facilitating effective label representation interactions without relying on explicit supervision from label co-occurrence signals. Based on this, we develop a pioneering contrastive co-occurrence learning framework, which operates on the instance-level label co-occurrence graph for multi-label image classification. This framework involves sequential processes of label representation learning followed by co-occurrence perception learning. Cross-entropy loss for label classification learning and contrastive loss for co-occurrence perception learning are used to jointly optimize the entire framework end-to-end. In this way, label representations can interact effectively, fully perceiving their co-occurrence relationships at the instance level, thereby significantly improving the performance in label recognition. Extensive experiments on public benchmarks demonstrate the superiority of the proposed framework in multi-label image classification. Codes are available on https://github.com/jasonseu/CoCo
Keywords:
Contrastive learning
co-occurrence learning
multi-label image classification
graph propagation
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W