Rich Human Feedback for Text-to-Image Generation

1 code implementation • 15 Dec 2023 • Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katie Collins, Yiwen Luo, Yang Li, Kai J Kohlhoff, Deepak Ramachandran, Vidhya Navalpakkam

We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.

Text-to-Image Generation

UniAR: Unifying Human Attention and Response Prediction on Visual Content

no code implementations • 15 Dec 2023 • Peizhao Li, Junfeng He, Gang Li, Rachit Bhargava, Shaolei Shen, Nachiappan Valliappan, Youwei Liang, Hongxiang Gu, Venky Ramachandran, Golnaz Farhadi, Yang Li, Kai J Kohlhoff, Vidhya Navalpakkam

Such a model would enable predicting subjective feedback such as overall satisfaction or aesthetic quality ratings, along with the underlying human attention or interaction heatmaps and viewing order, enabling designers and content-creation models to optimize their creation for human-centric improvements.

GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation

no code implementations • 27 Nov 2017 • Matan Sela, Pingmei Xu, Junfeng He, Vidhya Navalpakkam, Dmitry Lagun

Recent research has demonstrated the ability to estimate gaze on mobile devices by performing inference on the image from the phone's front-facing camera, and without requiring specialized hardware.

Gaze Estimation Image Generation +1

Predicting response time and error rates in visual search

no code implementations • NeurIPS 2011 • Bo Chen, Vidhya Navalpakkam, Pietro Perona

A model of human visual search is proposed.

TAR