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Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching

Wenjing Chen

TL;DR

This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel, significantly enhancing the model's ability to understand and represent the complex relationship between images and texts.

Abstract

With the rapid development of multimodal learning, the image-text matching task, as a bridge connecting vision and language, has become increasingly important. Based on existing research, this study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE). This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel, significantly enhancing the model's ability to understand and represent the complex relationship between images and texts. In addition, we adopt a parameterized feature fusion strategy to flexibly integrate feature information at different levels, further improving the model's expressive power. In terms of loss function design, the MH-CVSE model adopts a dynamic weight adjustment strategy to dynamically adjust the weight according to the loss value itself, so that the model can better balance the contribution of different loss terms during training. At the same time, we introduce a cosine annealing learning rate strategy to help the model converge more stably in the later stages of training. Extensive experimental verification on the Flickr30k dataset shows that the MH-CVSE model achieves better performance than previous methods in both bidirectional image and text retrieval tasks, fully demonstrating its effectiveness and superiority.

Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching

TL;DR

This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel, significantly enhancing the model's ability to understand and represent the complex relationship between images and texts.

Abstract

With the rapid development of multimodal learning, the image-text matching task, as a bridge connecting vision and language, has become increasingly important. Based on existing research, this study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE). This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel, significantly enhancing the model's ability to understand and represent the complex relationship between images and texts. In addition, we adopt a parameterized feature fusion strategy to flexibly integrate feature information at different levels, further improving the model's expressive power. In terms of loss function design, the MH-CVSE model adopts a dynamic weight adjustment strategy to dynamically adjust the weight according to the loss value itself, so that the model can better balance the contribution of different loss terms during training. At the same time, we introduce a cosine annealing learning rate strategy to help the model converge more stably in the later stages of training. Extensive experimental verification on the Flickr30k dataset shows that the MH-CVSE model achieves better performance than previous methods in both bidirectional image and text retrieval tasks, fully demonstrating its effectiveness and superiority.
Paper Structure (20 sections, 16 equations, 4 figures, 1 table)

This paper contains 20 sections, 16 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Self-Attention Mechanism and Multi-Head Self-Attention
  4. Exploration and improvement of feature fusion technology
  5. Dynamic Weight Adjustment of Loss Function and Learning Rate Scheduling Strategy
  6. MH-CVSE model
  7. Model Architecture
  8. Multi-head self-attention mechanism
  9. Parameterized Feature Fusion
  10. Dynamic Weight Adjustment of Loss Function
  11. Cosine Annealing Learning Rate Strategy
  12. Training and Inference
  13. Experiment
  14. Dataset and Settings
  15. Evaluation Metrics
  16. ...and 5 more sections

Figures (4)

  • Figure 1: MH-CVSE model integrates Faster R-CNN and Bi-GRU encoders with a multi-head self-attention mechanism for parallel image-text feature extraction. It employs parametric feature fusion (concat, $adap\_sum$, $weight\_sum$) and graph convolutional networks for consensus-aware learning. The model is further optimized with dynamic loss weights and cosine annealing learning rates for enhanced image-text matching performance.
  • Figure 2: Schematic of the multi-head self-attention mechanism. The process begins with encoding the input image or text features (X), which are then split into multiple attention heads. Each head computes attention using separate weight matrices ($W_Q$, $W_K$, $W_V$) for queries, keys, and values, producing outputs ($Z_1$, $Z_2$, ...,$Z_8$). These outputs are concatenated and transformed by a final weight matrix ($W_O$) to yield the final attention output (Z).
  • Figure 3: Image-text matching examples. From left to right: (1) a dog leaping off a dock, (2) a dog in a tug-of-war with a toy, (3) a man with a backpack on a mountain trail. Each image is paired with a textual description highlighting the main action and elementszheng2020dual.
  • Figure 4: Matching text: A girl wearing a red and multicolored bikini is laying on her back in shallow water .