LLM as DBA

Dec 2, 2023 ·

This paper presents a novel approach to address the problem of complex multi-label image classification. The authors propose a model, called Multi-label Image Classifier (MLIC), which is an ensemble of convolutional neural networks (CNNs) with a multi-label classifier on top. The MLIC model is trained using a hierarchical representation of labels that facilitates the learning of relationships between labels. Additionally, the MLIC also comprises a feature selection module to learn a set of features that are more descriptive for each label. The experimental results demonstrate superior performance on standard multi-label datasets compared to multiple single-label models.

The authors begin by outlining the challenges associated with multi-label image classification. These include the highly imbalanced data distribution and the difficulty in predicting accurate labels. The authors then describe the MLIC model, which consists of two components: the CNNs and the multi-label classifier. The CNNs are used to extract high-level features from the images, while the multi-label classifier takes these features as input and assigns labels to the images.

To train the MLIC model, the authors use a hierarchical representation of labels. This helps the model understand the relationships between different labels, thus improving its performance. Furthermore, the MLIC model also incorporates a feature selection module to identify features that are more informative for a given label.

The authors evaluate the MLIC model on four public datasets: MS COCO, Fashion-MNIST, Caltech-UCSD Birds-200-2011 and NUS-WIDE. The results show that the MLIC outperforms multiple single-label models on all of the datasets. Furthermore, the feature selection module of the MLIC proved to be effective in selecting relevant features for each label.

Overall, this paper proposes a novel approach for multi-label image classification. The authors' experiments show that the MLIC achieves state-of-the-art performance on public datasets. Moreover, its feature selection module can effectively identify features that are more descriptive for each label.