Leveraging Large Language Models and Vision-Language Models for Zero-Shot One-Class Visual Classification

Core Concepts

It is possible to discriminate between a single category and other semantically related ones using only its label by combining large language models and vision-language pre-trained models.

Abstract

The paper proposes a methodology for zero-shot one-class visual classification, where only the label of the target class is available. The key insights are:

Existing zero-shot classification approaches are not easily adapted to the one-class limit case, as they typically require multiple categories to build their classifiers.

The authors propose a two-step solution that first queries large language models (LLMs) for visually confusing objects and then relies on vision-language pre-trained models (e.g., CLIP) to perform classification.

The authors adapt existing large-scale vision datasets for one-class zero-shot classification, including a granularity-controlled version of iNaturalist, where negative samples are at a fixed distance in the taxonomy tree from the positive ones.

The authors demonstrate that their proposed method outperforms adapted off-the-shelf alternatives in this setting, showing the benefits of combining LLMs and vision-language models for one-class zero-shot classification.

The authors investigate different thresholding strategies, including fixed and adaptive thresholds, and show that combining the adaptive threshold using negative prompts with a fixed threshold from ImageNet1K leads to significant performance improvements.

Stats

We consider the problem of zero-shot one-class visual classification, where only the label of the target class is available.
The goal is to discriminate between positive and negative query samples without requiring any validation example from the target task.

Quotes

"We propose a methodology that combines vision-language pre-trained models with Large Language Models (LLMs) in a two-step procedure, where the LLM is used to suggest names of visually confusing categories that are incorporated to estimate the boundary of the target class."
"We validate our methodology on existing large-scale vision datasets adapted for zero-shot one-class classification."

Key Insights Distilled From

LLM meets Vision-Language Models for Zero-Shot One-Class Classification

by Yassir Bendo... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00675.pdf

Deeper Inquiries

How can the proposed methodology be extended to handle dynamic or evolving target classes, where the set of visually confusing objects may change over time?

In the context of dynamic or evolving target classes, where the visually confusing objects may change over time, the proposed methodology can be extended by implementing a continuous learning approach. This approach involves updating the model periodically with new data to adapt to changes in the target classes and the set of visually confusing objects. Here are some key strategies to handle dynamic target classes:

Incremental Learning: Implement an incremental learning strategy where the model is updated with new data incrementally without retraining the entire model from scratch. This allows the model to adapt to new target classes and visually confusing objects over time.

Active Learning: Incorporate an active learning mechanism where the model actively selects the most informative samples for labeling. By focusing on the most uncertain or challenging samples, the model can improve its performance on dynamic target classes.

Online Learning: Utilize online learning techniques that enable the model to learn from streaming data in real-time. This ensures that the model stays up-to-date with the latest information on target classes and visually confusing objects.

Self-supervised Learning: Integrate self-supervised learning methods that enable the model to learn representations from unlabeled data. By leveraging self-supervised learning, the model can adapt to new target classes and visually confusing objects without requiring labeled data for every new class.

By incorporating these strategies, the proposed methodology can be extended to handle dynamic or evolving target classes effectively, ensuring that the model remains robust and adaptable to changes over time.

How can the performance of the one-class classifier be further improved by incorporating additional information beyond the class label, such as textual descriptions or visual attributes?

To enhance the performance of the one-class classifier by incorporating additional information beyond the class label, such as textual descriptions or visual attributes, several approaches can be considered:

Multi-modal Fusion: Integrate both textual descriptions and visual attributes into a multi-modal framework. By fusing information from different modalities, the model can capture richer representations and improve classification accuracy.

Attention Mechanisms: Implement attention mechanisms to focus on relevant parts of the textual descriptions or visual attributes. This can help the model effectively utilize the additional information for better classification.

Fine-grained Features: Extract fine-grained features from textual descriptions and visual attributes to capture detailed information about the target class. Fine-grained features can enhance the discriminative power of the classifier.

Transfer Learning: Apply transfer learning techniques to leverage pre-trained models on textual or visual tasks. By transferring knowledge from these models, the classifier can benefit from learned representations and improve performance.

Data Augmentation: Augment the training data by incorporating variations in textual descriptions or visual attributes. Data augmentation techniques can help the model generalize better to unseen variations in the input data.

By incorporating these strategies and leveraging additional information beyond the class label, such as textual descriptions or visual attributes, the performance of the one-class classifier can be significantly improved, leading to more accurate and robust classification results.

What are the potential applications of the zero-shot one-class classification approach beyond the domains explored in this paper, and how can the method be adapted to address the unique challenges of those applications?

The zero-shot one-class classification approach has diverse applications beyond the domains explored in the paper. Some potential applications include:

Anomaly Detection: Zero-shot one-class classification can be applied to anomaly detection in various domains such as cybersecurity, healthcare, and manufacturing. By identifying anomalies based on a single class label, the approach can detect unusual patterns or behaviors.

Fraud Detection: In the financial sector, zero-shot one-class classification can be used for fraud detection. By learning to classify legitimate transactions based on a single class label, the approach can flag potentially fraudulent activities.

Medical Diagnosis: In healthcare, the approach can assist in medical diagnosis by identifying rare diseases or conditions based on limited information. This can help healthcare professionals in early detection and treatment.

To adapt the method for these applications and address their unique challenges, the following strategies can be employed:

Domain-specific Feature Engineering: Tailor the feature engineering process to extract relevant features specific to the application domain. This can enhance the model's ability to capture important characteristics for classification.

Domain-specific Data Augmentation: Implement data augmentation techniques that are specific to the application domain to generate diverse training samples. This can improve the model's robustness and generalization capabilities.

Expert Knowledge Incorporation: Integrate domain knowledge from experts to guide the model in learning relevant patterns and making accurate classifications. Expert input can help in refining the model's decision-making process.
By customizing the zero-shot one-class classification approach to suit the requirements of different applications and addressing their unique challenges through domain-specific strategies, the method can be effectively applied in a wide range of real-world scenarios.