Nachiappan Valliappan
Nachiappan Valliappan is a staff software engineer working on machine learning at Google Research. He obtained his Master's degree in Electrical & Computer Engineering from The University of Texas at Austin and his Bachelor'a degree from the College of Engineering, Guindy at Anna University. His full publication list can be found on his Google Scholar page.
Authored Publications
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UniAR: A Unified model for predicting human Attention and Responses on visual content
Peizhao Li
Gang Li
Rachit Bhargava
Shaolei Shen
Youwei Liang
Hongxiang Gu
Venky Ramachandran
Golnaz Farhadi
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Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes. Yet most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual content. We propose UniAR – a unified model of human attention and preference behavior across diverse visual content. UniAR leverages a multimodal transformer to predict subjective feedback, such as satisfaction or aesthetic quality, along with the underlying human attention or interaction heatmaps and viewing order. We train UniAR on diverse public datasets spanning natural images, webpages, and graphic designs, and achieve SOTA performance on multiple benchmarks across various image domains and behavior modeling tasks. Potential applications include providing instant feedback on the effectiveness of UIs/visual content, and enabling designers and content-creation models to optimize their creation for human-centric improvements.
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Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical role of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model; (2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner; and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli.
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We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private algorithm for this task and demonstrate its advantages over previous algorithms on several real-world datasets.
Our core algorithmic primitive is a differentially private procedure that takes in a set of distributions and produces an output that is close in Earth Mover's Distance (EMD) to the average of the inputs. We prove theoretical bounds on the error of our algorithm under certain sparsity assumption and that these are essentially optimal.
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Digital biomarker of mental fatigue
Vincent Wen-Sheng Tseng
Venky Ramachandran
Tanzeem Choudhury
npj Digital Medicine, 4 (2021), pp. 1-5
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Mental fatigue is an important aspect of alertness and wellbeing. Existing fatigue tests are subjective and/or time-consuming. Here, we show that smartphone-based gaze is significantly impaired with mental fatigue, and tracks the onset and progression of fatigue. A simple model predicts mental fatigue reliably using just a few minutes of gaze data. These results suggest that smartphone-based gaze could provide a scalable, digital biomarker of mental fatigue.
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Accelerating eye movement research via accurate and affordable smartphone eye tracking
Na Dai
Ethan Steinberg
Kantwon Rogers
Venky Ramachandran
Mina Shojaeizadeh
Li Guo
Nature Communications, 11 (2020)
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Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.
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On-device Few-shot Personalization for Real-time Gaze Estimation
Khoi Pham
Chase Riley Roberts
Dmitry Lagun
ICCV 2019 Gaze workshop
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Recent research has demonstrated the ability to estimate user’s gaze on mobile devices, by performing inference from an image captured with the phone’s front-facing camera, and without requiring specialized hardware. Gaze estimation accuracy is known to improve with additional calibration data from the user. However, most existing methods require either significant number of calibration
points or computationally intensive model fine-tuning that is practically infeasible on a mobile device. In this paper, we overcome limitations of prior work by proposing a novel few-shot personalization approach for 2D gaze estimation. Compared to the best calibration-free model [11], the proposed method yields substantial improvements in gaze prediction accuracy (24%) using only 3 calibration
points in contrast to previous personalized models that offer less improvement while requiring more calibration points. The proposed model requires 20x fewer FLOPS than the state-of-the-art personalized model [11] and can be run entirely on-device and in real-time, thereby unlocking a variety of important applications like accessibility, gaming and human-computer interaction.
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