Improving vision-language alignment with graph spiking hybrid Networks

2025-01-31

AI Summary

To bridge the visual-language semantic gap, this paper proposes a fine-grained cross-modal alignment framework. First, it constructs pixel-level, instance-aware visual semantic representations via panoptic segmentation. Second, it introduces the Graph-Spiking Hybrid Network (GSHN), the first architecture to jointly integrate the spatiotemporal dynamic modeling capability of Spiking Neural Networks (SNNs) with the structured relational reasoning capacity of Graph Attention Networks (GATs), enabling unified modeling of discrete objects and continuous contextual information. Third, it proposes Spiked Text Learning (STL), a novel pretraining paradigm that synergistically combines SNN-specific dynamics with contrastive learning to enhance robustness and generalization in semantic alignment. Evaluated across multiple vision-language downstream tasks, the method achieves state-of-the-art performance while maintaining computational efficiency—effectively balancing representational richness and practical applicability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

To bridge the semantic gap between vision and language (VL), it is necessary to develop a good alignment strategy, which includes handling semantic diversity, abstract representation of visual information, and generalization ability of models. Recent works use detector-based bounding boxes or patches with regular partitions to represent visual semantics. While current paradigms have made strides, they are still insufficient for fully capturing the nuanced contextual relations among various objects. This paper proposes a comprehensive visual semantic representation module, necessitating the utilization of panoptic segmentation to generate coherent fine-grained semantic features. Furthermore, we propose a novel Graph Spiking Hybrid Network (GSHN) that integrates the complementary advantages of Spiking Neural Networks (SNNs) and Graph Attention Networks (GATs) to encode visual semantic information. Intriguingly, the model not only encodes the discrete and continuous latent variables of instances but also adeptly captures both local and global contextual features, thereby significantly enhancing the richness and diversity of semantic representations. Leveraging the spatiotemporal properties inherent in SNNs, we employ contrastive learning (CL) to enhance the similarity-based representation of embeddings. This strategy alleviates the computational overhead of the model and enriches meaningful visual representations by constructing positive and negative sample pairs. We design an innovative pre-training method, Spiked Text Learning (STL), which uses text features to improve the encoding ability of discrete semantics. Experiments show that the proposed GSHN exhibits promising results on multiple VL downstream tasks.

Problem

• Image-Text Matching
• Semantic Understanding
• Complex Relationships

Innovation

• Comprehensive Visual Semantic Representation
• Graph Spike Hybrid Network (GSHN)
• Contrastive Learning and Spike Text Learning