Balanced Similarity with Auxiliary Prompts: Addressing Text-to-Image Retrieval Bias in CLIP for Zero-shot Learning

Core Concepts

CLIP suffers from text-to-image retrieval bias due to imbalanced similarity scores, addressed by Balanced Similarity with Auxiliary Prompts (BSAP) to enhance zero-shot learning performance.

Abstract

The content discusses the discovery of text-to-image retrieval bias in CLIP and proposes BSAP to mitigate this bias. By balancing similarity scores using auxiliary prompts, BSAP improves CLIP's performance in zero-shot learning tasks like Referring Expression Comprehension (REC) and Referring Image Segmentation (RIS). Experimental results show significant enhancements in accuracy and effectiveness across different datasets.

Stats

Specifically, when using the val dataset of RefCOCO in REC, BSAP increases CLIP’s performance by 20.6%.
The results in the REC task of all datasets were increased by an average point of 5.92%.
The balanced similarity score of the given query text is used for the final retrieval.
Extensive experiments on two typical zero-shot learning tasks demonstrate the effectiveness of our BSAP.
The experimental results demonstrate that BSAP improves the CLIP-based methods.

Quotes

"CLIP has a strong ability to understand the contents of dog and person images and text descriptions."
"Our BSAP designs auxiliary prompts for CLIP to calculate multiple similarity scores for retrieval images."
"We propose a balanced similarity with auxiliary prompts (BSAP) for CLIP to mitigate text-to-image retrieval bias."

Key Insights Distilled From

Balanced Similarity with Auxiliary Prompts

by Hanyao Wang,... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18400.pdf

Deeper Inquiries

How can other cross-modal foundation models benefit from applying Balanced Similarity with Auxiliary Prompts?

Other cross-modal foundation models can benefit from applying Balanced Similarity with Auxiliary Prompts by addressing biases in text-to-image retrieval, similar to what was observed in CLIP. By using auxiliary prompts to balance similarity scores and normalize them across different objects or categories, these models can improve their performance in zero-shot learning tasks. This approach helps mitigate bias caused by imbalanced ranges of similarity scores and enhances the model's ability to accurately match images with textual descriptions. Additionally, incorporating hybrid similarity that combines original similarities with balanced similarities can lead to more robust outcomes for various tasks.

What are potential implications of addressing image-to-text retrieval bias using similar methodologies?

Addressing image-to-text retrieval bias using methodologies like Balanced Similarity with Auxiliary Prompts can have several implications. Firstly, it can significantly improve the accuracy and performance of cross-modal models in zero-shot learning tasks by reducing biases that affect text-to-image matching. This improvement leads to better generalization and adaptability of the model across different datasets and scenarios. Secondly, mitigating bias through balanced similarity approaches enhances the interpretability and reliability of model predictions, making them more trustworthy for real-world applications such as image captioning, object detection, and semantic segmentation.

How might varying template lengths impact the performance of Balanced Similarity with Auxiliary Prompts?

Varying template lengths in Balanced Similarity with Auxiliary Prompts could impact its performance in several ways:

Template Specificity: Longer templates may provide more context or information related to the query text but could also introduce noise or irrelevant details if not carefully crafted.
Model Understanding: Shorter templates might be easier for the model to process quickly but may lack sufficient information for accurate alignment between texts and images.
Complex Queries: Longer templates could handle complex queries better by providing additional cues or constraints for matching images effectively.
Generalization: Optimal template length selection is crucial for generalizing well on unseen data; finding a balance between specificity and generality is key.
Computational Efficiency: Longer templates may increase computational complexity during inference due to higher input dimensions; shorter templates could streamline processing but at the risk of oversimplification.

By experimenting with different template lengths based on task requirements and dataset characteristics, one can optimize the performance of Balanced Similarity methods tailored to specific use cases within cross-modal foundation models like CLIP.