Efficient Test-Time Personalization for Gaze Estimation with Meta Prompt

Core Concepts

Efficient personalization of gaze estimation using meta prompt at test time.

Abstract

The article introduces a method for efficient and accurate personalization of gaze estimation without labeled data. It leverages prompt tuning techniques inspired by natural language processing research to update a small group of parameters, known as the "prompt," during personalization. The proposed method aligns unsupervised loss with gaze error through meta-learning, ensuring effective adaptation even with minimal data. Extensive experiments demonstrate significant improvements in accuracy and adaptation speed compared to state-of-the-art methods.

Stats

The proposed method can be 10 times faster in terms of adaptation speed than compared methods.
The prompt can contain less than 1% of a ResNet-18's parameters.

Quotes

"Our experiments show high efficiency of the prompt tuning approach."
"Our method significantly outperforms existing methods in terms of adaptation speed and accuracy."

Key Insights Distilled From

Test-Time Personalization with Meta Prompt for Gaze Estimation

by Huan Liu,Jul... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2401.01577.pdf

Deeper Inquiries

How can the concept of prompt tuning be applied to other computer vision tasks

Prompt tuning can be applied to other computer vision tasks by adapting the concept of updating a small group of parameters, namely the "prompt," while keeping the rest of the network fixed. This approach allows for efficient personalization at test time without needing to update all parameters in the network. By incorporating prompt tuning into other computer vision tasks, models can be quickly adapted to specific inputs or conditions without requiring extensive retraining. For example, in object detection tasks, prompt tuning could involve modifying certain input features or layers based on specific object characteristics or environmental conditions.

What are the potential limitations or challenges of using meta-learning for initializing prompts

One potential limitation or challenge of using meta-learning for initializing prompts is ensuring that the meta-learned initialization generalizes well across different individuals or scenarios. Meta-learning relies on learning an ideal initialization that aligns with minimizing gaze error through unsupervised losses like symmetry loss. However, if the meta-initialized prompt does not effectively capture variations in individual characteristics or fails to adapt appropriately during personalization, it may lead to suboptimal performance. Additionally, designing an effective meta-learning strategy requires careful consideration of hyperparameters and model architecture to ensure successful alignment between unsupervised losses and gaze estimation error.

How might this method impact real-world applications beyond gaze estimation

This method could have significant impacts on real-world applications beyond gaze estimation by enabling personalized experiences in various domains such as healthcare, gaming, human-computer interaction (HCI), and more. For instance:

Healthcare: In healthcare settings, personalized medical imaging analysis could benefit from prompt tuning for identifying specific patterns related to patient conditions.
Autonomous Vehicles: Prompt tuning could enhance object recognition systems in autonomous vehicles by adapting quickly to changing road conditions and environments.
Retail: Personalized recommendation systems in retail could use this method to tailor product suggestions based on individual preferences captured through visual cues.
Security: Video surveillance systems could leverage prompt tuning for improved facial recognition accuracy under varying lighting conditions.

By applying this method across diverse applications, it has the potential to enhance user experiences and optimize performance tailored specifically to individual needs or contexts within these domains.