CLIP-Gaze: Leveraging Vision-Language Models for Gaze Estimation

Conceitos essenciais

CLIP-Gaze introduces a novel framework that leverages vision-language models to enhance gaze estimation by addressing diverse data types and improving generalization capabilities. The approach involves extracting gaze-relevant features and refining their distribution to achieve state-of-the-art performance in cross-domain evaluations.

Resumo

The content discusses the development of CLIP-Gaze, a framework that utilizes vision-language models for gaze estimation tasks. It addresses the challenges of domain generalization in gaze estimation by leveraging pre-trained models and personalized context optimization methods. The proposed approach significantly improves performance in cross-domain evaluations, demonstrating the effectiveness of the framework.
Key points include:

Introduction to gaze estimation and its applications.
Challenges faced by existing methods due to domain gaps.
Proposal of CLIP-Gaze framework leveraging vision-language models.
Extraction of gaze-relevant features through text prompts.
Refinement of feature distribution for improved generalization.
Experimental results showcasing superior performance over existing methods.
The study highlights the importance of leveraging vision-language models for enhancing gaze estimation tasks, particularly in addressing domain gaps and improving generalization capabilities.

Estatísticas

Existing methods try to address domain gaps using various approaches but face limitations due to dataset diversity.
CLIP-Gaze leverages a pre-trained vision-language model for transferable knowledge in gaze estimation tasks.
Extensive experiments demonstrate excellent performance over existing methods on cross-domain evaluations.

Citações

"CLIP-Gaze is the first to leverage the vision-and-language cross-modality approach for gaze estimation task."
"Our proposed framework achieves state-of-the-art performance on domain generalization for gaze estimation tasks."

Principais Insights Extraídos De

CLIP-Gaze

by Pengwei Yin,... às arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05124.pdf

Perguntas Mais Profundas

How can personalized context optimization impact other computer vision tasks beyond gaze estimation

Personalized context optimization can have a significant impact on various computer vision tasks beyond gaze estimation. By tailoring text prompts to individual characteristics or attributes, the model can better understand and interpret visual information in a personalized manner. This approach could enhance tasks like object recognition, image classification, and scene understanding by providing context-specific cues that improve accuracy and generalization across diverse datasets. For instance, in object recognition, personalized prompts could help the model focus on specific features or attributes unique to different objects, leading to more precise identification even in complex scenes with multiple objects.

What are potential ethical considerations when implementing advanced AI frameworks like CLIP-Gaze

Implementing advanced AI frameworks like CLIP-Gaze raises several ethical considerations that need careful attention. One key concern is privacy and data security since these models often require large amounts of personal data for training purposes. Ensuring proper consent from individuals whose data is used and implementing robust data protection measures are essential to safeguard privacy rights. Additionally, bias and fairness issues may arise if the model inadvertently perpetuates stereotypes or discriminates against certain groups based on biased training data. It's crucial to address bias through thorough dataset curation and algorithmic transparency to mitigate potential harm.

How might incorporating additional modalities, such as audio or touch, enhance the capabilities of CLIP-Gaze

Incorporating additional modalities such as audio or touch into CLIP-Gaze can significantly expand its capabilities and applicability across various domains. By integrating audio input, the model could perform tasks like lip reading or sound source localization alongside gaze estimation for more comprehensive human-computer interaction systems. Touch modality integration could enable tactile feedback analysis in conjunction with visual cues for applications requiring haptic interactions or gesture recognition. This multimodal approach enhances user experience by capturing a broader range of sensory inputs for richer contextual understanding and improved task performance.