Learning Interpretable Visual Classifiers from Images Using Large Language Models

Core Concepts

A novel method that discovers interpretable yet discriminative sets of attributes for visual recognition by integrating large language models and evolutionary search.

Abstract

The paper presents a framework for learning interpretable visual recognition systems from image data. The key idea is to tightly integrate evolutionary search with large language models (LLMs) to efficiently learn discrete, discriminative attributes that are interpretable. The method works as follows: It maintains a bank of hypotheses for potential attribute sets per class, and iteratively mutates them using an LLM. The LLM leverages in-context learning to predict strong mutations that reduce the classification loss. To encourage common attributes at the beginning, a pre-training strategy is used to first discover attributes that separate one class from all others. The method is evaluated on fine-grained classification of specialized scientific concepts in the iNaturalist dataset, as well as on completely novel concepts in the Kiki-Bouba dataset. The results show that the proposed method outperforms various baselines, including those that have access to privileged information like class names. The learned attributes are shown to be interpretable and discriminative. The ability to audit dataset bias is highlighted as an advantage of the interpretable approach.

Stats

"Multimodal pre-trained models, such as CLIP, are popular for zero-shot classification due to their open-vocabulary flexibility and high performance." "Our method outperforms the latest baselines by 18.4% on five fine-grained iNaturalist datasets and by 22.2% on two KikiBouba datasets, despite the baselines having access to privileged information about class names."

Quotes

"Multimodal foundation models like CLIP [1] obtain excellent performance on many visual recognition tasks due to their flexibility to represent open-vocabulary classes. These models have the potential to impact many scientific applications, where computer vision systems could automate recognition in specialized domains. However, since foundation models are neural networks, they are largely black-box and we therefore have no means to explain or audit the predictions they produce, limiting their trust." "Our primary contribution is a framework for learning interpretable visual recognition systems from image data. We propose to tightly integrate evolutionary search with large language models, allowing us to efficiently learn discrete, discriminative attributes that are interpretable."

Key Insights Distilled From

Evolving Interpretable Visual Classifiers with Large Language Models

by Mia Chiquier... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09941.pdf

Deeper Inquiries

How can the proposed framework be extended to handle multi-label classification tasks where an image may contain multiple objects or concepts?

In order to extend the proposed framework to handle multi-label classification tasks, where an image may contain multiple objects or concepts, several modifications and enhancements can be implemented: Attribute Set Modification: Instead of having a single set of attributes for each class, the framework can be adjusted to allow for multiple attribute sets per class. Each attribute set can represent a different concept or object within the image, enabling the model to capture the presence of multiple objects simultaneously. Scoring Mechanism: The scoring mechanism can be adapted to consider the presence of multiple attributes across different sets. The model can assign scores to each attribute set independently and then aggregate these scores to make predictions for multiple labels. Loss Function: The loss function used for optimization can be modified to account for the multi-label nature of the classification task. Techniques such as binary cross-entropy or multi-label classification loss functions can be employed to train the model to predict multiple labels for each image. Training Data: The training data should be annotated with multiple labels for images that contain more than one object or concept. This annotated data will be crucial for training the model to recognize and predict multiple labels accurately. By incorporating these adjustments, the framework can be extended to effectively handle multi-label classification tasks, allowing it to identify and classify multiple objects or concepts within an image simultaneously.

What are the potential limitations of using large language models as the mutation mechanism in the evolutionary search, and how can these be addressed?

While using large language models (LLMs) as the mutation mechanism in evolutionary search offers several advantages, there are also potential limitations that need to be considered: Computational Resources: LLMs are computationally expensive and require significant resources for training and inference. This can lead to longer training times and higher costs, especially when scaling up the model for complex tasks. Data Efficiency: LLMs may require large amounts of data to learn meaningful patterns and generate accurate mutations. Limited data availability or data biases can impact the effectiveness of the mutations generated by the LLM. Interpretability: Despite being able to generate interpretable attributes, LLMs may still produce mutations that are difficult to interpret or may introduce biases based on the training data. Ensuring the interpretability and fairness of the mutations is crucial. These limitations can be addressed by: Model Optimization: Optimizing the architecture and hyperparameters of the LLM to improve efficiency and reduce computational costs. Data Augmentation: Using data augmentation techniques to increase the diversity of training data and improve the robustness of the mutations generated by the LLM. Bias Mitigation: Implementing bias detection and mitigation strategies to ensure that the mutations generated by the LLM are fair and unbiased. By addressing these limitations, the use of LLMs as the mutation mechanism in evolutionary search can be optimized for improved performance and reliability.

Can the learned interpretable attributes be leveraged to improve the performance of black-box vision models like CLIP on specialized domains, and if so, how?

Yes, the learned interpretable attributes can be leveraged to enhance the performance of black-box vision models like CLIP on specialized domains by providing more transparent and meaningful insights into the classification process. Here's how this can be achieved: Fine-tuning CLIP: The learned interpretable attributes can be used to fine-tune the pre-trained CLIP model on specialized domains. By incorporating these attributes into the training process, the model can better understand and recognize specific visual concepts relevant to the specialized domain. Feature Attribution: The interpretable attributes can serve as feature attributions that explain why a particular prediction was made by the black-box model. This can help in identifying the key visual cues or characteristics that contribute to the model's decision-making process. Model Explainability: By using the interpretable attributes, it becomes easier to explain the predictions of the black-box model to stakeholders or end-users. This transparency can increase trust in the model and provide valuable insights into its decision-making process. Bias Detection: The learned attributes can also be used to detect and mitigate biases in the black-box model. By analyzing the attributes associated with different predictions, biases can be identified and addressed to improve the model's fairness and accuracy. By leveraging the learned interpretable attributes in conjunction with black-box vision models like CLIP, it is possible to enhance performance, interpretability, and fairness in specialized domains, ultimately leading to more reliable and trustworthy AI systems.