Dynamic Visual Semantic Sub-Embeddings and Fast Re-Ranking

TL;DR

This paper introduces a Dynamic Visual Semantic Sub-Embeddings framework to reduce redundancy in image-text matching, coupled with a fast re-ranking strategy, improving semantic variation encoding and retrieval accuracy.

Contribution

It proposes a novel method for generating diverse, low-entropy visual sub-embeddings and an efficient re-ranking approach for cross-modal retrieval tasks.

Findings

Improved retrieval accuracy on MSCOCO, Flickr30K, and CUB datasets.

Effective encoding of semantic variations in visual embeddings.

Enhanced efficiency with the fast re-ranking strategy.

Abstract

The core of cross-modal matching is to accurately measure the similarity between different modalities in a unified representation space. However, compared to textual descriptions of a certain perspective, the visual modality has more semantic variations. So, images are usually associated with multiple textual captions in databases. Although popular symmetric embedding methods have explored numerous modal interaction approaches, they often learn toward increasing the average expression probability of multiple semantic variations within image embeddings. Consequently, information entropy in embeddings is increased, resulting in redundancy and decreased accuracy. In this work, we propose a Dynamic Visual Semantic Sub-Embeddings framework (DVSE) to reduce the information entropy. Specifically, we obtain a set of heterogeneous visual sub-embeddings through dynamic orthogonal constraint loss. To encourage the generated candidate embeddings to capture various semantic variations, we construct a mixed distribution and employ a variance-aware weighting loss to assign different weights to the optimization process. In addition, we develop a Fast Re-ranking strategy (FR) to efficiently evaluate the retrieval results and enhance the performance. We compare the performance with existing set-based method using four image feature encoders and two text feature encoders on three benchmark datasets: MSCOCO, Flickr30K and CUB Captions. We also show the role of different components by ablation studies and perform a sensitivity analysis of the hyperparameters. The qualitative analysis of visualized bidirectional retrieval and attention maps further demonstrates the ability of our method to encode semantic variations.