Nearest Neighbor Normalization Improves Multimodal Retrieval
Neil Chowdhury, Franklin Wang, Sumedh Shenoy, Douwe Kiela, Sarah Schwettmann, Tristan Thrush·October 31, 2024
Summary
Nearest Neighbor Normalization (NNN) enhances contrastive image-text retrieval models, improving performance in tasks like image captioning and cross-modal retrieval. This training-free method corrects errors in models using a reference database, boosting retrieval metrics for various models and datasets. NNN addresses the hubness problem, optimizing parameters through a hyperparameter search, and shows consistent gains over original models when normalizing with out-of-distribution queries. It efficiently reduces bias, particularly gender bias, in image retrieval, improving accuracy without significant performance drops. NNN's effectiveness is demonstrated across different models and datasets, making it a flexible solution for settings without an obvious reference database.
Introduction
Background
Overview of contrastive image-text retrieval models
Importance of NNN in enhancing retrieval performance
Objective
Aim of NNN in correcting errors in models
Goals of NNN in improving retrieval metrics
Method
Data Collection
Source of reference databases for NNN
Types of data used for training and testing
Data Preprocessing
Techniques for preparing data for NNN
Handling of out-of-distribution queries
Hyperparameter Search
Methodology for optimizing NNN parameters
Impact of hyperparameters on retrieval performance
Bias Reduction
Focus on gender bias in image retrieval
Strategies for minimizing bias through NNN
Results
Performance Metrics
Improvement in retrieval metrics with NNN
Comparison with original models
Dataset and Model Compatibility
Application of NNN across different models and datasets
Flexibility of NNN in various settings
Case Studies
Detailed Examples
Illustration of NNN's effectiveness in specific scenarios
Comparison of retrieval results before and after NNN application
Conclusion
Summary of NNN's contributions
Future Directions
Potential improvements and extensions of NNN
Research gaps and opportunities for further exploration
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
What is Nearest Neighbor Normalization (NNN) and how does it enhance contrastive image-text retrieval models?
In what types of settings is NNN demonstrated to be effective, and how does it handle the challenge of an out-of-distribution query for normalization?
What are the specific improvements NNN shows in terms of retrieval metrics and bias reduction, particularly in image retrieval?
How does NNN address the hubness problem in models and what is the process for optimizing its parameters?