Learning Transferable Negative Prompts for Robust Out-of-Distribution Detection

Temel Kavramlar

NegPrompt learns a set of negative prompts, each representing a negative connotation of a given in-distribution class label, to enhance the vision-language model's sensitivity towards unknown out-of-distribution samples.

Özet

The paper proposes a novel out-of-distribution (OOD) detection method called NegPrompt that leverages pre-trained vision-language models (VLMs) like CLIP. The key idea is to learn a set of negative prompts, each representing a negative connotation of a given in-distribution (ID) class label, to delineate the boundaries between ID and OOD images.
The main highlights are:

NegPrompt learns the negative prompts using only the ID training data, without relying on any external outlier data, addressing the mismatch between OOD images and ID categories that plagues existing prompt learning-based OOD detection methods.

The learned negative prompts are transferable to novel class labels, enabling NegPrompt to work in open-vocabulary learning scenarios where the inference stage can contain ID classes not present during training.

Extensive experiments on ImageNet-based benchmarks show that NegPrompt consistently outperforms state-of-the-art prompt learning-based OOD detection methods in both conventional and hard OOD detection settings.

İstatistikler

The paper does not provide any specific numerical data or statistics in the main text.

Alıntılar

"NegPrompt aims to utilize ID training data and positive prompts (i.e., the text prompt embeddings of ID classes) to learn such negative prompts in a way to which OOD images exhibits higher similarity than ID images."
"Essentially, the learned negative prompts have similar semantics to the text prompts generated from the 'no' text encoder in CLIPN, but NegPrompt presents a fundamentally different approach: it capitalizes on the generalization ability of the CLIP model and learns the negative prompts with training ID data only, eliminating the reliance on external data and the extensive computation overhead as in CLIPN."

Önemli Bilgiler Şuradan Elde Edildi

Learning Transferable Negative Prompts for Out-of-Distribution Detection

by Tianqi Li,Gu... : arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03248.pdf

Daha Derin Sorular

How can the transferability of the learned negative prompts be further improved to handle a wider range of novel ID classes during inference?

To enhance the transferability of the learned negative prompts for handling a broader range of novel ID classes during inference, several strategies can be implemented:

Augmented Training Data: By augmenting the training data with a diverse set of examples from various ID classes, the negative prompts can learn more generalized features that can be applied to a wider range of novel classes during inference.

Fine-tuning on Similar Classes: Fine-tuning the negative prompts on classes that are semantically similar to the novel ID classes can help in transferring the learned features effectively. This can be achieved by identifying classes with overlapping characteristics and fine-tuning the prompts on these classes.

Regularization Techniques: Implementing regularization techniques during the training of negative prompts can prevent overfitting to specific ID classes and promote the learning of more transferable features that can be applied to novel classes.

Multi-Task Learning: Incorporating a multi-task learning approach where the negative prompts are trained not only on the existing ID classes but also on auxiliary tasks that require generalization across a wider range of classes can improve transferability.

Adversarial Training: Introducing adversarial training to the learning process of negative prompts can help in generating more robust and transferable features that are effective in distinguishing between ID and OOD classes, including novel ID classes.

By implementing these strategies, the transferability of the learned negative prompts can be enhanced to handle a wider range of novel ID classes during inference.

What other types of negative semantics, beyond class-specific negative prompts, could be explored to enhance the OOD detection capabilities of VLMs?

In addition to class-specific negative prompts, several other types of negative semantics can be explored to enhance the OOD detection capabilities of Vision-Language Models (VLMs):

Attribute-based Negative Prompts: Negative prompts that focus on specific attributes or features present in OOD images but absent in ID classes can be utilized. By learning prompts that capture these distinctive attributes, VLMs can better differentiate between ID and OOD samples.

Contextual Negative Prompts: Negative prompts that consider the contextual information surrounding the objects in an image can be beneficial. By incorporating contextual cues that are indicative of OOD scenarios, VLMs can improve their OOD detection capabilities.

Temporal Negative Prompts: Negative prompts that leverage temporal information or sequential patterns in images can be explored. By considering the temporal context of images, VLMs can identify anomalies or OOD samples based on changes over time.

Spatial Negative Prompts: Negative prompts that focus on spatial relationships within an image can be effective in detecting OOD samples. By learning prompts that capture irregular spatial configurations or structures, VLMs can enhance their OOD detection abilities.

Domain-specific Negative Prompts: Negative prompts tailored to specific domains or datasets can be valuable. By incorporating domain-specific knowledge or features into the prompts, VLMs can improve their ability to detect OOD samples within that particular domain.

By exploring these different types of negative semantics in addition to class-specific negative prompts, VLMs can enhance their OOD detection capabilities and improve their overall performance in distinguishing between ID and OOD samples.

How can the proposed NegPrompt approach be extended to other vision-language tasks beyond OOD detection, such as zero-shot classification or open-vocabulary learning?

The NegPrompt approach can be extended to other vision-language tasks beyond OOD detection, such as zero-shot classification or open-vocabulary learning, by adapting the methodology to suit the specific requirements of these tasks:

Zero-Shot Classification: For zero-shot classification tasks, NegPrompt can be modified to learn prompts that facilitate the recognition of novel classes without prior training data. By training negative prompts on a diverse set of known classes and leveraging the transferability of these prompts, VLMs can effectively classify images belonging to unseen classes.

Open-Vocabulary Learning: In open-vocabulary learning scenarios, where the model needs to classify images from both known and novel classes, NegPrompt can be utilized to learn negative prompts that capture the distinguishing features of novel classes. By training on a subset of known classes and generating negative prompts for unseen classes, VLMs can adapt to new vocabulary seamlessly.

Fine-Tuning for Specific Tasks: By fine-tuning the negative prompts on task-specific data and adjusting the learning objectives to align with the requirements of zero-shot classification or open-vocabulary learning, NegPrompt can be tailored to excel in these tasks.

Multi-Modal Learning: Extending NegPrompt to incorporate multi-modal inputs, such as text and images, can further enhance its performance in zero-shot classification and open-vocabulary learning tasks. By jointly learning negative prompts for both modalities, VLMs can improve their understanding of novel classes.

By customizing the NegPrompt approach and integrating it with the appropriate techniques for zero-shot classification and open-vocabulary learning, VLMs can achieve superior performance in a variety of vision-language tasks beyond OOD detection.