Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval Analysis

Core Concepts

The author proposes L2RM, a framework based on Optimal Transport, to rematch mismatched pairs in cross-modal retrieval, addressing the challenge of semantically irrelevant data harming performance.

Abstract

The paper introduces L2RM to address Partially Mismatched Pairs (PMPs) in cross-modal retrieval by learning refined alignments through Optimal Transport. The method aims to improve robustness against real-world scenarios where mismatched pairs are common. Previous methods have focused on down-weighting the contribution of PMPs, but L2RM learns from explicit similarity-cost mapping relations and models a partial OT problem to boost refined alignments. Extensive experiments demonstrate the effectiveness of L2RM in improving existing models' robustness against PMPs.

Stats

Conceptual Captions dataset contains about 3% to 20% mismatched pairs.
Flickr30K dataset consists of 31,000 images with five corresponding text annotations each.
MS-COCO dataset collects 123,287 images with five sentences each.

Quotes

"Undoubtedly, such semantical irrelevant data will remarkably harm the cross-modal retrieval performance."
"A question naturally arises: Could cross-modal retrieval models even learn useful knowledge from mismatched pairs?"

Key Insights Distilled From

Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval

by Haochen Han,... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05105.pdf

Deeper Inquiries

How can L2RM's approach be applied to other domains beyond cross-modal retrieval

L2RM's approach can be applied to other domains beyond cross-modal retrieval by adapting the concept of rematching mismatched pairs to different types of data. For example, in natural language processing tasks such as sentiment analysis or text classification, where noisy or mislabeled data can hinder model performance, L2RM could be used to identify and rematch mismatched text samples. By leveraging the potential semantic similarity among unpaired samples, L2RM could help improve the robustness of models in these domains.

What counterarguments exist against the use of rematching mismatched pairs in cross-modal retrieval

Counterarguments against the use of rematching mismatched pairs in cross-modal retrieval may include concerns about overfitting to noise in the data. Rematching pairs based on potential semantic similarity among initially mismatched samples could lead to a biased representation if not carefully implemented. Additionally, there might be challenges in defining an effective cost function that accurately captures the true matching relations between samples. Critics may argue that relying too heavily on rematching could introduce biases and distortions into the model's learning process.

How does self-supervised learning play a role in optimizing cost functions for robust cross-modal retrieval

Self-supervised learning plays a crucial role in optimizing cost functions for robust cross-modal retrieval by enabling the automatic learning of transport costs from explicit similarity-cost mapping relations. In the context of L2RM, self-supervised learning allows for the dynamic adjustment and refinement of cost functions based on reconstructed visual-text pairs with reserved matching indexes. This approach helps guide the cost function towards minimizing transport costs while considering ideal matching probabilities for each pair. By incorporating self-supervised learning techniques, L2RM can adaptively learn from training data without requiring manual annotation or supervision, enhancing its ability to handle partially mismatched pairs effectively.