Arya Farkhondeh

I am a PhD student at EPFL (École polytechnique fédérale de Lausanne) & Idiap Research Institute in Switzerland, where I work on computer vision, deep learning, and human-centric scene understanding under the supervision of Jean-Marc Odobez. Previously, I was a Research Intern at the Computer Vision Center (CVC) in Barcelona, where I worked with Sergio Escalera. I received my M.Sc. degree in Data Science from Sapienza University of Rome, Italy.

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I am interested in Computer Vision, Multimodal Learning, Unsupervised Learning, and Human-Centric Scene Understanding.

We introduce a novel framework to jointly predict the gaze target and social gaze label for all people in the scene. It comprises: (i) a temporal, transformer-based architecture that, in addition to image tokens, handles person-specific tokens capturing the gaze information related to each individual; (ii) a new dataset, VSGaze, that unifies annotation types across multiple gaze following and social gaze datasets. We show that our model trained on VSGaze can address all tasks jointly, and achieves state-of-the-art results for multi-person gaze following and social gaze prediction.

We investigate the zero-shot capabilities of Vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues.

CCDb-HG is a novel and large dataset for head gesture recognition, featuring diverse head gesture categories. Furthermore, we explore the inclusion of gaze as an auxiliary cue, along with geometric and temporal data augmentation, to enhance generalization. Additionally, we evaluate various model architectures to establish baseline performance on CCDb-HG.

SwAT is a self-supervised approach for gaze estimation that outperforms previous state-of-the-art methods and supervised baselines on several benchmarks.

A new approach for detecting anomalies in surveillance videos that uses long-range dependencies from trajectories and basic motion information.

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