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Michael Louis Iuzzolino

4 accepted papers

2025

Gaze-Language Alignment for Zero-Shot Prediction of Visual Search Targets from Human Gaze Scanpaths

Sounak Mondal, Naveen Sendhilnathan, Ting Zhang, Yue Liu, Michael Proulx, Michael Louis Iuzzolino +2
ICCV 2025poster

Decoding human intent from eye gaze during a visual search task has become an increasingly important capability within augmented and virtual reality systems. However, gaze target prediction models used within such systems are constrained by the predefined target categories found within available gaz…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Pretrained Language Models as Visual Planners for Human Assistance

Dhruvesh Patel, Hamid Eghbalzadeh, Nitin Kamra, Michael Louis Iuzzolino, Unnat Jain, Ruta Desai
ICCV 2023poster

In our pursuit of advancing multi-modal AI assistants capable of guiding users to achieve complex multi-step goals, we propose the task of 'Visual Planning for Assistance (VPA)'. Given a succinct natural language goal, e.g., "make a shelf", and a video of the user's progress so far, the aim of VPA i…

Cited by 43PDF ↗code ↗Scholar ↗
2021

Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss

Michael Louis Iuzzolino, Michael Curtis Mozer, Samy Bengio
NeurIPS 2021poster

Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics. Deep nets typically operate in serial stages wherein each layer completes its computation before processing begins in subsequent layers. In contrast, biologica…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2021

Wandering within a world: Online contextualized few-shot learning

Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard Zemel
ICLR 2021poster

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learnin…

Cited by 41Source ↗PDF ↗code ↗Scholar ↗