4 accepted papers
Recent Vision-Language Models (VLMs) have demonstrated remarkable multimodal understanding capabilities, yet the redundant visual tokens incur prohibitive computational overhead and degrade inference efficiency. Prior studies typically relies on [CLS] attention or text-vision cross-attention to iden
Gaze estimator computes the gaze direction based on face images. Most existing gaze estimation methods perform well under within-dataset settings, but can not generalize to unseen domains. In particular, the ground-truth labels in unseen domain are often unavailable. In this paper, we propose a new…
Recent advances of deep learning-based approaches have achieved remarkable performance on appearance-based gaze estimation. However, due to the shortage of target domain data and absence of target labels, generalizing gaze estimation algorithm to unseen environments is still challenging. In this pap…
Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained model to new domains, e.g., unseen environments or persons. In this paper, we propose a plug-and-play gaze adaptation fr…