Noisy-Correspondence Learning for Text-to-Image Person Re-identification

Core Concepts

Proposing a Robust Dual Embedding method (RDE) to address noisy correspondence in Text-to-Image Person Re-identification, achieving state-of-the-art results.

Abstract

The content introduces the problem of noisy correspondence in TIReID and proposes the RDE method to mitigate its impact. It consists of Confident Consensus Division (CCD) and Triplet Alignment Loss (TAL) components. Extensive experiments on three benchmarks show RDE's robustness and superiority over existing methods. Introduction to Text-to-image person re-identification. Problem of noisy correspondence in training data. Proposal of Robust Dual Embedding method (RDE). Components: Confident Consensus Division (CCD) and Triplet Alignment Loss (TAL). Experiments on three public benchmarks showcasing RDE's performance.

Stats

Our RDE achieves 75.94%, 90.14%, and 94.12% in terms of Rank-1,5,10 on the 'Best' rows under 0% noise. The proposed TAL outperforms widely-used Triplet Ranking Loss (TRL) and SDM loss [24]. On CUHK-PEDES with 50% noise, RDE achieves 71.33%, 87.41%, and 91.81% in terms of Rank-1,5,10 on the 'Best' rows.

Quotes

"The model does not know which pairs are noisy in practice." "Our RDE can achieve robustness against NC thanks to the proposed reliable supervision and stable triplet loss."

Key Insights Distilled From

Noisy-Correspondence Learning for Text-to-Image Person Re-identification

by Yang Qin,Yin... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2308.09911.pdf

Deeper Inquiries

How can noisy correspondence affect real-world applications beyond TIReID

Noisy correspondence can have significant implications beyond just TIReID in real-world applications. In scenarios like surveillance systems, where person tracking and identification are crucial, noisy correspondences can lead to misidentifications and false alarms. This could result in security breaches, wrongful accusations, or missed opportunities to track down suspects or missing persons. In medical imaging analysis, noisy correspondences could impact the accuracy of diagnoses and treatment plans, potentially leading to incorrect decisions that affect patient outcomes. Additionally, in e-commerce platforms utilizing text-to-image matching for product recommendations or search results, noisy correspondences may result in irrelevant suggestions or inaccurate matches for users.

What counterarguments exist against using a dual embedding approach like RDE

While a dual embedding approach like RDE offers robustness against noisy correspondences in TIReID tasks, there are some potential counterarguments that could be raised: Complexity: Introducing dual embeddings adds complexity to the model architecture and training process. This complexity may require more computational resources and longer training times. Overfitting: There is a risk of overfitting when using multiple embedding modules if not carefully optimized during training. Interpretability: Dual embeddings might make it harder to interpret how features from different modalities interact with each other compared to simpler models. Generalization: The effectiveness of dual embeddings may vary across different datasets and tasks; therefore, generalizing its performance across all scenarios might be challenging.

How might advancements in vision-language pre-training models impact the effectiveness of methods like RDE

Advancements in vision-language pre-training models can significantly impact the effectiveness of methods like RDE: Improved Representations: Vision-language pre-trained models provide better representations for both visual and textual data by leveraging large-scale pre-training on diverse datasets. Enhanced Cross-Modal Understanding: These models capture rich semantic relationships between images and texts through self-supervised learning objectives during pre-training. Transfer Learning Benefits: By fine-tuning these pre-trained models on specific tasks like TIReID with methods like RDE, one can leverage the learned cross-modal interactions for improved performance without starting from scratch. 4Adaptability: As vision-language pre-training continues to evolve with larger datasets and more sophisticated architectures (e.g., CLIP), methods like RDE can adapt these advancements into their frameworks for even better results. These advancements pave the way for more effective cross-modal retrieval systems by providing stronger foundations for understanding image-text relationships at scale while addressing challenges such as noise robustness encountered in real-world applications beyond TIReID."