Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification (1912.07872v2)

Abstract: Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our novel cross-modality attention maps are generated with the guidance of learned label embeddings. Experiments on two multi-label image classification datasets (MS-COCO and NUS-WIDE) show our method outperforms other existing state-of-the-arts. In addition, we validate our method on a large multi-label video classification dataset (YouTube-8M Segments) and the evaluation results demonstrate the generalization capability of our method.

• The paper presents a novel framework integrating semantic graph embedding and cross-modality attention to enhance multi-label classification performance.
• The framework uses adjacency-based graph embedding for label semantics and a cross-modality attention mechanism guided by these embeddings to focus on discriminative image regions.
• Experiments on MS-COCO, NUS-WIDE, and YouTube-8M show the method consistently outperforms state-of-the-art techniques across various metrics, demonstrating its effectiveness and generalizability.

Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification

The paper "Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification" introduces a novel approach to address the complexities inherent in multi-label classification tasks within computer vision. The primary focus is on enhancing the predictive accuracy for multi-label image and video classification problems by explicitly modeling semantic dependencies between labels and improving the localization of discriminative features.

Overview

Multi-label classification requires the capability to predict multiple categories present in a single image or video, which contrasts with single-label tasks that associate one category per instance. This paper proposes leveraging semantic graph embedding linked with a cross-modality attention mechanism to tackle the existing challenges efficiently.

Methodology

The proposed framework is constructed around two essential modules:

1. Semantic Graph Embedding (SGE): The methodology employs an adjacency-based similarity graph embedding technique to learn semantic embeddings for labels, which capitalizes on conditional probabilities derived from training data. This graph models label relationships and is used to guide attention within the deep learning model.
2. Cross-Modality Attention (CMA): This novel attention mechanism uses the learned semantic embeddings to direct the focus of the neural network model onto specific visual regions in an image or video, enhancing the discriminative power of feature extraction by aligning image features with label semantics.

The paper introduces an optimization relaxation technique for the semantic graph embedding to manage the graph sparsity effectively and ensure optimization convergence. Additionally, cross-modality attention is applied via multi-scale scenes to cover different feature representation scales ensuring better generalization and robustness of the model.

Experimental Results

The effectiveness of the proposed methodology is validated through extensive experiments on well-known datasets for both image (MS-COCO and NUS-WIDE) and video (YouTube-8M Segments) classifications. Quantitative results indicate that the proposed method consistently outperforms existing state-of-the-art approaches across multiple metrics such as mean Average Precision (mAP), highlighting its ability to generalize across different scales and modalities. Notably, the Multi-Scale CMA further improves performance compared to a single-scale approach, which affirms the benefits of having multiple resolutions of features.

Implications

The research provides significant insights into dealing with multi-label classification by effectively integrating label semantics into attention mechanisms. The cross-modality attention mechanism's ability to leverage these semantic relationships to guide feature extraction could reshape how large-scale multi-label data is processed in applications ranging from surveillance to content recommendation systems.

Future Directions

The potential future research directions could explore expanding the model's applicability to different types of data beyond image and video, such as text or audio, further diversifying its application scope. Another promising avenue is the exploration of more sophisticated graph embedding techniques or attention mechanisms that consider more complex spatial or temporal dependencies, potentially improving the model's robustness and accuracy in real-world scenarios.

In summary, the paper contributes significantly to the field of multi-label classification by presenting a method that successfully integrates semantic information into the model, resulting in improved classification performance across diverse datasets.