Towards Pixel-Level Prediction for Gaze Following: Benchmark and Approach

TL;DR

This paper introduces GazeSeg, a novel pixel-level gaze target prediction method utilizing a visual foundation model and a new dataset, significantly improving accuracy in natural scene gaze following tasks.

Contribution

The paper presents GazeSeg, a new unified framework for gaze target segmentation and recognition, and releases a comprehensive dataset with pixel-level annotations for gaze following.

Findings

Achieves Dice score of 0.325 in gaze segmentation

Attains 71.7% top-5 recognition accuracy

Outperforms previous state-of-the-art in gaze-following AUC

Abstract

Following the gaze of other people and analyzing the target they are looking at can help us understand what they are thinking, and doing, and predict the actions that may follow. Existing methods for gaze following struggle to perform well in natural scenes with diverse objects, and focus on gaze points rather than objects, making it difficult to deliver clear semantics and accurate scope of the targets. To address this shortcoming, we propose a novel gaze target prediction solution named GazeSeg, that can fully utilize the spatial visual field of the person as guiding information and lead to a progressively coarse-to-fine gaze target segmentation and recognition process. Specifically, a prompt-based visual foundation model serves as the encoder, working in conjunction with three distinct decoding modules (e.g. FoV perception, heatmap generation, and segmentation) to form the framework for gaze target prediction. Then, with the head bounding box performed as an initial prompt, GazeSeg obtains the FoV map, heatmap, and segmentation map progressively, leading to a unified framework for multiple tasks (e.g. direction estimation, gaze target segmentation, and recognition). In particular, to facilitate this research, we construct and release a new dataset, comprising 72k images with pixel-level annotations and 270 categories of gaze targets, built upon the GazeFollow dataset. The quantitative evaluation shows that our approach achieves the Dice of 0.325 in gaze target segmentation and 71.7% top-5 recognition. Meanwhile, our approach also outperforms previous state-of-the-art methods, achieving 0.953 in AUC on the gaze-following task. The dataset and code will be released.