Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as artifacts/implausibility, misalignment with text descriptions, and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models, prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper, we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text, and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images (RichHF-18K) and train a multimodal transformer to predict the rich feedback automatically. 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. Notably, the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants). The RichHF-18K data set will be released in our GitHub repository: https://github.com/google-research/google-research/tree/master/richhf_18k.

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.

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.

Mobile Search experiences have evolved significantly from a few blue links that require users to click. Recent search and ad units surface instant information to the user in a variety of visually rich formats that include images, horizontal swipes, and vertical scrolls. These innovative experiences call for new metrics and models to better understand searcher behavior on mobile phones. In this paper, we study how the presence of ads and their formats impacts searcher's gaze and satisfaction. We systematically vary presentation format of the sponsored result, while controlling for other factors, such as position and quality of organic results. We experiment with several configurations of text ad and rich ad formats. Our findings indicate that showing rich ad formats improve search experience, by drawing more attention to the information-rich ad and allowing users to interact to view more offers, which increases user satisfaction with search. In addition, we extend prior work by comparing the performance of various models to infer user's gaze from viewport data. Our models improve accuracy of existing viewport-based gaze inference methods by 30% in Pearson's correlation. Together, our findings show that viewport data can be used for fast, accurate and scalable measurement of user attention on a per-element basis, for both ads as well as organic search results.

Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant information on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measurement and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Focusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and viewport duration on a per result basis, and that the average user attention is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.

The potential for user-relevant, context-appropriate, targeted advertising on mobile devices is enormous given device improvements and advances in personal and location-based data collection. However, little is known about how users experience display advertisements ('ads') on mobile devices, or what factors drive mobile ad effectiveness. In this paper, we investigate users' experiences of display advertising on mobile devices. We consider three factors that are often studied in desktop settings the ad's level of personal relevance to the user, its relevance to the page content, and within-ad properties, with a particular focus on the level of animation in the ad. Our findings reveal a few surprises. First, personal relevance to the user has little or no impact on ad efficacy measured by recall. Instead, content relevance boosts ad recall. Second, user relevance leads to a more pleasant and interesting experience, but content relevance has no effect. Third, contrary to the popular notion that animation often leads to more effective ads by garnering more user attention, we find that a simple type of animation, such as blinking animation, negatively affects user experience and reduces ad recall. Our findings, while focused on advertising, offer insights for design of mobile content presentation in general.

As search pages are becoming increasingly complex, with images and nonlinear page layouts, understanding how users examine the page is important. We present a lab study on the effect of a rich informational panel to the right of the search result column, on eye and mouse behavior. Using eye and mouse data, we show that the flow of user attention on nonlinear page layouts is different from the widely believed top-down linear examination order of search results. We further demonstrate that the mouse, like the eye, is sensitive to two key attributes of page elements -- their position (layout), and their relevance to the user's task. We identify mouse measures that are strongly correlated with eye movements, and develop models to predict user attention (eye gaze) from mouse activity. These findings show that mouse tracking can be used to infer user attention and information flow patterns on search pages. Potential applications include ranking, search page optimization, and UI evaluation.

We study how the visual catchiness (saliency) of relevant information impacts user engagement metrics such as focused attention and emotion (affect). Participants completed tasks in one of two conditions, where the task-relevant information either appeared salient or non-salient. Our analysis provides insights into relationships between saliency, focused attention, and affect. Participants reported more distraction in the non-salient condition, and non-salient information was slower to find than salient. Lack-of-saliency led to a negative impact on affect, while saliency maintained positive affect, suggesting its helpfulness. Participants reported that it was easier to focus in the salient condition, although there was no significant improvement in the focused attention scale rating. Finally, this study suggests user interest in the topic is a good predictor of focused attention, which in turn is a good predictor of positive affect. These results suggest that enhancing saliency of user-interested topics seems a good strategy for boosting user engagement.

Previous studies have used mouse tracking as a tool to measure usability of webpages, user attention and search relevance. In this paper, we go beyond measurement of user behavior to prediction of the resulting user experience from mouse patterns alone. Specifically, we identify mouse markers that can predict user frustration and reading struggles at reasonably high accuracy. We believe that mouse-based prediction of user experience is an important advance, and could potentially offer a scalable way to infer user experience on the web. In addition, we demonstrate that mouse tracking could be used for applications such as evaluating content layout and content noticeability; we apply this in particular to advertisements. More generally, it could be used to infer user attention in complex webpages containing images, text and varied content, including how attention patterns vary with page layout and user distraction.

One of the most important categories of online advertising is display advertising which provides publishers with significant revenue. Similar to other categories, the main goal in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Previous studies have tried to optimize these parameters using objectives such as behavioral targeting. However, there is no published work so far to address the effect of the visual appearance of ads (creatives) on user response rate via a systematic data-driven approach. In this paper, we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and others are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the highest impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.

Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC) and cost-per-action (CPA). An accurate prediction of the probability that users click on ads is a crucial task in NGD advertising because this value is required to compute the expected revenue. State-of-the-art prediction algorithms rely heavily on historical information collected for advertisers, users and publishers. Click prediction of new ads in the system is a challenging task due to the lack of such historical data. The objective of this paper is to mitigate this problem by integrating multimedia features extracted from display ads into the click prediction models. Multimedia features can help us capture the attractiveness of the ads with similar contents or aesthetics. In this paper we evaluate the use of numerous multimedia features (in addition to commonly used user, advertiser and publisher features) for the purposes of improving click prediction in ads with no history. We provide analytical results generated over billions of samples and demonstrate that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.

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.

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.

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.