Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze Estimation

核心概念

A novel deep learning model, STAGE, that leverages spatial attention and temporal sequence modeling to accurately estimate gaze direction from video sequences. The model is further personalized using Gaussian processes to adapt to individual-specific traits with just a few labeled samples.

要約

The content presents a novel deep learning model, STAGE, for video gaze estimation. The key highlights are:

STAGE employs a Spatial Attention Module (SAM) to focus on gaze-relevant spatial changes between consecutive frames, effectively filtering out irrelevant distractors like facial expressions or background movements.

STAGE incorporates a Temporal Sequence Model (TSM) to capture the dynamic evolution of gaze direction across the video sequence, enabling improved gaze prediction.

The authors integrate Gaussian processes (GPs) to personalize the STAGE model for individual users, requiring only a few labeled samples. The GP-based personalization provides uncertainty estimates in addition to point predictions, making the approach more suitable for practical applications.

Extensive experiments on three public video gaze datasets demonstrate that STAGE outperforms state-of-the-art methods in both within-dataset and cross-dataset settings. Specifically, STAGE achieves state-of-the-art performance on the Gaze360 dataset, improving the mean angular error by 2.5° without personalization and an additional 0.8° with just 3 personalization samples.

The qualitative analysis shows that the SAM module effectively focuses on the eye region and suppresses irrelevant distractors, highlighting its importance for video gaze estimation.

統計

"Gaze is an essential prompt for analyzing human behavior and attention."
"Video gaze estimation faces significant challenges, such as understanding the dynamic evolution of gaze in video sequences, dealing with static backgrounds, and adapting to variations in illumination."
"Our proposed approach achieves state-of-the-art performance on the Gaze360 dataset, improving by 2.5°without personalization. Further, by personalizing the model with just three samples, we achieved an additional improvement of 0.8°."

引用

"Realizing the potential of spatial and motion cues in videos, prior research has utilized residual frames and optical flows for several other vision tasks [13, 58, 68]."
"Similar to prior works [44, 52], our approach utilizes a spatial attention mechanism to focus on gaze-relevant information while minimizing the impact of distractors."
"Consistent with this approach, we integrate Gaussian processes (GPs) [54], known for their effectiveness in low-data scenarios, to personalize the STAGE model for individual users."

抽出されたキーインサイト

Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze Estimation

by Swati Jindal... 場所 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05215.pdf

深掘り質問

How can the proposed STAGE model be extended to handle longer video sequences and capture long-term gaze dynamics?

To extend the STAGE model for longer video sequences and capture long-term gaze dynamics, several modifications can be considered:

Hierarchical Attention Mechanisms: Implementing hierarchical attention mechanisms can help the model focus on different levels of spatial and temporal details in the video sequences. This can enable the model to capture long-term dependencies and subtle gaze dynamics over extended periods.
Memory-Augmented Networks: Incorporating memory-augmented networks can allow the model to store and retrieve relevant information from past frames in the video sequence. This can enhance the model's ability to maintain context over longer sequences and improve gaze estimation accuracy.
Recurrent Neural Networks (RNNs) with Attention: Utilizing RNNs with attention mechanisms can help the model retain information from previous frames and selectively attend to important spatial and temporal features. This can facilitate the modeling of long-term gaze dynamics in video sequences.

How can the insights from the video gaze estimation task be applied to other computer vision problems that involve analyzing dynamic spatial and temporal information, such as action recognition or video understanding?

The insights from video gaze estimation can be applied to other computer vision tasks involving dynamic spatial and temporal information in the following ways:

Action Recognition: By leveraging attention mechanisms similar to those used in gaze estimation, models for action recognition can focus on relevant spatial and temporal cues in video sequences. This can improve the identification of key actions and movements in dynamic scenes.
Video Understanding: Techniques such as spatial and temporal attention can enhance video understanding tasks by highlighting important regions and moments in videos. This can aid in tasks like object detection, activity recognition, and scene understanding by capturing relevant spatial and temporal dynamics.
Human-Robot Interaction: Applying gaze estimation insights to human-robot interaction can enable robots to understand human attention and intentions better. By analyzing gaze patterns and dynamics, robots can adapt their behavior and responses to enhance communication and collaboration with humans.

What other personalization techniques, beyond Gaussian processes, could be explored to further improve the model's adaptation to individual users?

In addition to Gaussian processes, the following personalization techniques could be explored to enhance the model's adaptation to individual users:

Meta-Learning: Meta-learning techniques can be employed to quickly adapt the model to new users with limited data. By learning from a diverse set of users during training, the model can generalize better to new individuals with minimal labeled samples.
Siamese Networks: Siamese networks can be utilized to learn user-specific features and embeddings for gaze estimation. By comparing similarities and differences between users, the model can tailor its predictions to individual characteristics.
Online Learning: Implementing online learning strategies can enable the model to continuously adapt and update its parameters based on new user interactions. This real-time adaptation can improve the model's performance and personalization over time.