A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction

Core Concepts

Introducing a novel framework for multi-person temporal gaze following and social gaze prediction.

Abstract

The article presents a new framework for multi-person temporal gaze following and social gaze prediction. It addresses the limitations of previous approaches by jointly predicting gaze targets and social gaze labels for all individuals in a scene. The proposed architecture includes a temporal, transformer-based model that handles person-specific tokens to capture individual gaze information. A new dataset, VSGaze, unifies annotation types across multiple datasets for training the model. Experimental results show state-of-the-art performance in multi-person gaze following and social gaze prediction tasks.

Stats

Most images are annotated for a single person with their head bounding box and gaze target point. The model processes the image only once for all people in the scene. The loss coefficients are set as λHM = 1000, λVEC = 3, λIO = 2, and λLAH = λSA = 1.

Quotes

"Our architecture achieves state-of-the-art results for multi-person gaze following and social gaze prediction." "Existing methods for gaze following suffer from several drawbacks." "The vast majority of gaze following approaches have proposed static models that can handle only one person at a time."

Key Insights Distilled From

A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction

by Anshul Gupta... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10511.pdf

Deeper Inquiries

How can incorporating auxiliary information like speaking status improve the results further?

Incorporating auxiliary information like speaking status can enhance the model's performance by providing additional context to the gaze prediction task. When a person is speaking, their gaze behavior may differ from when they are listening or engaged in other activities. By including this information in the model, it can learn to differentiate between different states and improve its predictions accordingly. This additional feature helps capture more nuanced behaviors and contributes to a more comprehensive understanding of human communication dynamics.

What are the potential implications of leveraging multiple datasets with different statistics on model performance?

Leveraging multiple datasets with varying statistics poses both challenges and opportunities for model performance. On one hand, combining diverse datasets allows for a broader range of training examples, potentially leading to a more robust and generalizable model. The varied data sources provide exposure to different scenarios, contexts, and behaviors that can enrich the learning process. However, integrating datasets with distinct characteristics also introduces complexities such as dataset bias, domain shift, or conflicting annotations. These factors could impact the model's ability to generalize across all datasets equally well. Therefore, careful preprocessing steps and tuning strategies are essential to ensure that the model effectively learns from each dataset while mitigating any negative effects caused by differences in statistics.

How might this novel framework impact other applications beyond human communication behaviors?

This novel framework for multi-person temporal gaze following and social gaze prediction has far-reaching implications beyond just human communication behaviors: Human-Robot Interaction: The framework could be applied in robotics settings where robots need to understand human intentions through gaze cues during interactions. This could lead to more intuitive and responsive robotic systems. Medical Diagnosis: Gaze patterns play a crucial role in various medical conditions such as autism spectrum disorder or attention deficit hyperactivity disorder (ADHD). The framework could assist in diagnosing these conditions based on individuals' gaze behavior. Market Research: Understanding consumer behavior through eye-tracking studies is vital for market research purposes. This framework could aid in analyzing customer engagement with products or advertisements. Security Systems: Gaze tracking technology can be utilized in security systems for monitoring suspicious activities or identifying potential threats based on people's visual attention patterns. Overall, this innovative framework opens up possibilities for enhancing various applications that rely on interpreting human visual cues and social interactions.