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Vidhya Navalpakkam

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4 papers

1 author row

NeurIPS Conference 2024 Conference Paper

UniAR: A Unified model for predicting human Attention and Responses on visual content

Peizhao Li
Junfeng He
Gang Li
Rachit Bhargava
Shaolei Shen
Nachiappan Valliappan
Youwei Liang
Hongxiang Gu

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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AAAI Conference 2023 Conference Paper

Differentially Private Heatmaps

Badih Ghazi
Junfeng He
Kai Kohlhoff
Ravi Kumar
Pasin Manurangsi
Vidhya Navalpakkam
Nachiappan Valliappan

We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate its advantages over previous algorithms on real-world datasets. Our core algorithmic primitive is a DP 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 a certain sparsity assumption and that these are essentially optimal.

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NeurIPS Conference 2011 Conference Paper

Predicting response time and error rates in visual search

Bo Chen
Vidhya Navalpakkam
Pietro Perona

A model of human visual search is proposed. It predicts both response time (RT) and error rates (RT) as a function of image parameters such as target contrast and clutter. The model is an ideal observer, in that it optimizes the Bayes ratio of tar- get present vs target absent. The ratio is computed on the firing pattern of V1/V2 neurons, modeled by Poisson distributions. The optimal mechanism for integrat- ing information over time is shown to be a ‘soft max’ of diffusions, computed over the visual field by ‘hypercolumns’ of neurons that share the same receptive field and have different response properties to image features. An approximation of the optimal Bayesian observer, based on integrating local decisions, rather than diffusions, is also derived; it is shown experimentally to produce very similar pre- dictions. A psychophyisics experiment is proposed that may discriminate between which mechanism is used in the human brain.

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NeurIPS Conference 2005 Conference Paper

Optimal cue selection strategy

Vidhya Navalpakkam
Laurent Itti

Survival in the natural world demands the selection of relevant visual cues to rapidly and reliably guide attention towards prey an d predators in cluttered environments. We investigate whether our visu al system selects cues that guide search in an optimal manner. We formall y obtain the optimal cue selection strategy by maximizing the signal to noise ratio (S N R) between a search target and surrounding distractors. This optimal strategy successfully accounts for several phenom ena in visual search behavior, including the effect of target-distracto r discriminability, uncertainty in target's features, distractor heterogenei ty, and linear separability. Furthermore, the theory generates a new predict ion, which we verify through psychophysical experiments with human subj ects. Our results provide direct experimental evidence that humans sel ect visual cues so as to maximize S N R between the targets and surrounding clutter.

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