GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval

• URL: http://arxiv.org/abs/2505.13306v1
• Date: Mon, 19 May 2025 16:25:55 GMT
• Title: GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval
• Authors: Chengsong Sun, Weiping Li, Xiang Li, Yuankun Liu, Lianlei Shan,
• Abstract summary: Few-shot cross-modal retrieval focuses on learning cross-modal representations with limited training samples.<n>Existing methods often fail to adequately model the multi-peak distribution of few-shot cross-modal data.<n>We introduce a new strategy for cross-modal semantic alignment, which constrains the relative distances between image and text feature distributions.
• Score: 13.928213494843744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot cross-modal retrieval focuses on learning cross-modal representations with limited training samples, enabling the model to handle unseen classes during inference. Unlike traditional cross-modal retrieval tasks, which assume that both training and testing data share the same class distribution, few-shot retrieval involves data with sparse representations across modalities. Existing methods often fail to adequately model the multi-peak distribution of few-shot cross-modal data, resulting in two main biases in the latent semantic space: intra-modal bias, where sparse samples fail to capture intra-class diversity, and inter-modal bias, where misalignments between image and text distributions exacerbate the semantic gap. These biases hinder retrieval accuracy. To address these issues, we propose a novel method, GCRDP, for few-shot cross-modal retrieval. This approach effectively captures the complex multi-peak distribution of data using a Gaussian Mixture Model (GMM) and incorporates a multi-positive sample contrastive learning mechanism for comprehensive feature modeling. Additionally, we introduce a new strategy for cross-modal semantic alignment, which constrains the relative distances between image and text feature distributions, thereby improving the accuracy of cross-modal representations. We validate our approach through extensive experiments on four benchmark datasets, demonstrating superior performance over six state-of-the-art methods.

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