Source-linked AI summary
Position Focused Attention Network for Image-Text Matching
Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, Xin Fan
TL;DR
Image-text matching requires reliable similarity measurement across heterogeneous visual and textual modalities. PFAN integrates object position through block-based position representations and position-focused attention to enhance region expressions and visual-text relationships. The method achieves state-of-the-art performance on Flickr30K, MS-COCO, and Tencent-News.
Problem
Image-text matching must measure similarity between heterogeneous visual and textual contents while modeling their fine-grained correspondences.
Method
PFAN splits images into blocks, embeds relative block positions, and uses position-focused attention to generate position features combined with visual region features.
Results
PFAN achieves state-of-the-art performance on Flickr30K, MS-COCO, and Tencent-News.
Takeaways & Limitations
Integrating region position helps PFAN model a more reliable relationship between images and text across public and practical news datasets.
Abstract
from arXiv · showhide
Image-text matching tasks have recently attracted a lot of attention in the computer vision field. The key point of this cross-domain problem is how to accurately measure the similarity between the visual and the textual contents, which demands a fine understanding of both modalities. In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views. In this work, we integrate the object position clue to enhance the visual-text joint-embedding learning. We first split the images into blocks, by which we infer the relative position of region in the image. Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical large-scale news dataset (Tencent-News) to validate the practical application value of proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method achieves the state-of-art performance on all of these three datasets.
1 Introduction
Image-text matching must relate rich visual and textual content across heterogeneous modalities, yet existing attention mechanisms overlook relative region position. PFAN incorporates position-aware features and attention to improve image-region and word correspondences, achieving state-of-the-art performance on Flickr30K, MS-COCO, and Tencent-News.
- Image-text matching requires both modality-specific content representations and a cross-modal similarity measure for heterogeneous visual and textual information.
- Existing attention mechanisms model correspondences between visual regions and textual items but ignore relative position information within images.
- PFAN designs a position feature and position-focused attention mechanism to integrate region position into image-word correspondence modeling.
- The position feature is combined with visual features to form a more reliable and complete image-region representation.
- PFAN achieves state-of-the-art performance on Flickr30K, MS-COCO, and a practical Tencent-News dataset.
2 Our Approach
PFAN integrates object-position information with visual region features to improve image-text matching. It represents regions using position-aware features, aligns regions and words with attention, and trains the network with triplet ranking loss.
- Framework: PFAN extracts visual and positional features, combines them into final region representations, and studies region-word alignments with visual-textual attention.The network is trained using triplet ranking loss.
- Initial Position Representation: Images are divided into K×K blocks, and each region is assigned the indexes of its top L blocks by overlap.The overlap is measured by intersecting pixels between the region and each block.
- Block Embedding: Block indexes are embedded into dense vectors so the position of each image region can be represented more accurately.The blocks form a position vocabulary, and an embedding layer projects one-hot block representations into dense vectors.
- Position Focused Attention: Position-focused attention assigns adaptive weights to blocks for each region instead of relying only on overlap rates.The mechanism distinguishes completely covered blocks and gives greater importance to blocks with larger covered areas.
- Image-Sentence Relevance Calculation: The resulting position representation is concatenated with each visual region feature, while word features are produced by a bidirectional GRU for relevance calculation.Image-sentence relevance averages word-level relevance scores based on attended visual vectors and cosine similarity.
3 Experiments
PFAN is evaluated on Flickr30K, MS-COCO, and Tencent-News using retrieval metrics and visualization analyses. The reported results show strong retrieval performance and position-aware attention patterns, while the learned position embeddings preserve locality.
- 3.1 Experimental Setup: PFAN is evaluated on Flickr30K, MS-COCO, and Tencent-News using cross-modal retrieval metrics, including R@H, MAP, and accuracy.Tencent-News is a practical Chinese news dataset, where the task focuses on retrieving images for news titles.
- 3.2 Performance Evaluation: 70.0 R@1 is achieved by the fused PFAN t-i+i-t model for sentence retrieval on Flickr30K, outperforming the cited fused SCAN method.PFAN t-i reaches recalls of 66.0, 89.6, and 94.3 at depths 1, 5, and 10, respectively, for sentence retrieval given an image.
- 3.2 Performance Evaluation: PFAN is reported to outperform competing methods on MS-COCO and achieve state-of-the-art performance across Flickr30K, MS-COCO, and Tencent-News.The supplied results passage states that Table 2 supports similar conclusions and that Table 3 reports Tencent-News performance using MAP and accuracy.
- 3.3 Result Visualization: The learned position embeddings generally assign higher similarity to closer blocks, indicating that the embeddings preserve spatial locality.The similarity matrix is computed from block position embeddings and adjacent embeddings; the authors also note highly similar marginal blocks and lower similarities for some central blocks.
- 3.3 Result Visualization: The attention visualization shows that words generally attend to related semantic regions and that brighter blocks identify more important parts within attended regions.The visualization displays the six highest-weight blocks for each region, with brighter blocks representing higher weights.
4 Conclusion and Future Work
PFAN incorporates region-position information into image-text matching to model more reliable relationships between visual regions and words. It achieves state-of-the-art performance on Flickr30K, MS-COCO, and Tencent-News, while future work will fuse more semantic information.
- PFAN infers each region’s relative position from image blocks and generates a position feature through position-focused attention.The position and visual features are concatenated into the final region representation.
- The resulting region representation helps model a more reliable relation between image and text.
- PFAN achieves state-of-the-art performance on Flickr30K, MS-COCO, and the practical Tencent-News dataset.The paper presents Tencent-News as a practical dataset for evaluating application value.
- Future work will fuse more semantic information to enhance cross-domain relation learning.