Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system. Existing studies, though have explored various aspects of VQA but somewhat ignored this particular attribute. This paper aims to bridge the research gap by contributing a comprehensive dataset, called UNK-VQA. The dataset is specifically designed to address the challenge of questions that models do not know. To this end, we first augment the existing data via deliberate perturbations on either the image or question. In specific, we carefully ensure that the question-image semantics remain close to the original unperturbed distribution. By this means, the identification of unanswerable questions becomes challenging, setting our dataset apart from others that involve mere image replacement. We then extensively evaluate the zero- and few-shot performance of several emerging multi-modal large models and discover their significant limitations when applied to our dataset. Additionally, we also propose a straightforward method to tackle these unanswerable questions. This dataset, we believe, will serve as a valuable benchmark for enhancing the abstention capability of VQA models, thereby leading to increased trustworthiness of AI systems. We have made the dataset available to facilitate further exploration in this area.

Attending selectively to emotion-eliciting stimuli is intrinsic to human vision. In this research, we investigate how emotion-elicitation features of images relate to human selective attention. We create the EMOtional attention dataset (EMOd). It is a set of diverse emotion-eliciting images, each with (1) eye-tracking data from 16 subjects, (2) image context labels at both object- and scene-level. Based on analyses of human perceptions of EMOd, we report an emotion prioritization effect: emotion-eliciting content draws stronger and earlier human attention than neutral content, but this advantage diminishes dramatically after initial fixation. We find that human attention is more focused on awe eliciting and aesthetic vehicle and animal scenes in EMOd. Aiming to model the above human attention behavior computationally, we design a deep neural network (CASNet II), which includes a channel weighting subnetwork that prioritizes emotion-eliciting objects, and an Atrous Spatial Pyramid Pooling (ASPP) structure that learns the relative importance of image regions at multiple scales. Visualizations and quantitative analyses demonstrate the model's ability to simulate human attention behavior, especially on emotion-eliciting content.

People often prefer videos over images in research and applications, believing that videos are more effective for eliciting human emotions and building machine intelligence. However, our research shows that this assumption is not always correct when it comes to evoking emotions in human observers. In this article, we compare thirteen emotions and two perceptions elicited by short videos (2-6 second, silent video clips) versus static frames extracted from the videos. We show that static frames and videos elicit most emotions similarly, but static frames elicit negative emotions more strongly than videos. We test two complementary explanations: differential activation of suspense and the peak-end rule. These findings help us to computationally model human reactions more faithfully with fewer video frames. Our interdisciplinary results have important implications for methods, theory, and applications in diverse fields, including social psychology, computer vision, mass media, and marketing.

Image captioning is gaining significance in multiple applications such as content-based visual search and chat-bots. Much of the recent progress in this field embraces a data-driven approach without deep consideration of human behavioural characteristics. In this paper, we focus on human-centered automatic image captioning. Our study is based on the intuition that different people will generate a variety of image captions for the same scene, as their knowledge and opinion about the scene may differ. In particular, we first perform a series of human studies to investigate what influences human description of a visual scene. We identify three main factors: a person's knowledge level of the scene, opinion on the scene, and gender. Based on our human study findings, we propose a novel human-centered algorithm that is able to generate human-like image captions. We evaluate the proposed model through traditional evaluation metrics, diversity metrics, and human-based evaluation. Experimental results demonstrate the superiority of our proposed model on generating diverse human-like image captions.

Despite the recent success in face recognition and object classification, in the field of human gaze prediction, computer models are still struggling to accurately mimic human attention. One main reason is that visual attention is a complex human behavior influenced by multiple factors, ranging from low-level features (e.g., color, contrast) to high-level human perception (e.g., objects interactions, object sentiment), making it difficult to model computationally. In this work, we investigate the relation between object sentiment and human attention. We first introduce a new evaluation metric (AttI) for measuring human attention that focuses on human fixation consensus. A series of empirical data analyses with AttI indicate that emotion-evoking objects receive attention favor, especially when they co-occur with emotionally-neutral objects, and this favor varies with different image complexity. Based on the empirical analyses, we design a deep neural network for human attention prediction which allows the attention bias on emotion-evoking objects to be encoded in its feature space. Experiments on two benchmark datasets demonstrate its superior performance, especially on metrics that evaluate relative importance of salient regions. This research provides the clearest picture to date on how object sentiments influence human attention, and it makes one of the first attempts to model this phenomenon computationally.

Live streaming has become a new form of entertainment, which attracts hundreds of millions of users worldwide. The huge amount of multimedia data in live streaming platforms creates tremendous opportunities for online advertising. However, existing state-of-the-art video advertising strategies (e.g., pre-roll and contextual mid-roll advertising) that rely on analyzing the whole video, are not applicable to live streaming videos. This paper describes a novel monetization framework, named LiveSense, for live streaming videos, which is able to display a contextually relevant ad at a suitable timestamp in a non-intrusive way. Specifically, given a live streaming video, we first employ a deep neural network to determine whether the current moment is appropriate for displaying an ad using the historical streaming data. Then, we detect a set of candidate ad insertion areas by incorporating image saliency, background map, and location priorities, so that the ad is displayed over the non-important area. We introduce three types of relevance metrics including textual relevance, global visual relevance and local visual relevance to select the contextually relevant ad. To minimize user intrusiveness, we initially display the ad at a non-important area. If the user is interested in the ad, we will show the ad in an overlaid window with a translucent background. Empirical evaluation on a real-world dataset demonstrates that our proposed framework is able to effectively display ads in live streaming videos while maintaining users' online experience.

Privacy concerns with social media have recently been under the spotlight, due to a few incidents on user data leakage on social networking platforms. With the current advances in machine learning and big data, computer algorithms often act as a first-step filter for privacy breaches, by automatically selecting content with sensitive information, such as photos that contain faces or vehicle license plate. In this paper we propose a novel algorithm to protect the sensitive attributes against machines, meanwhile keeping the changes imperceptible to humans. In particular, we first conducted a series of human studies to investigate multiple factors that influence human sensitivity to the visual changes. We discover that human sensitivity is influenced by multiple factors, from low-level features such as illumination, texture, to high-level attributes like object sentiment and semantics. Based on our human data, we propose for the first time the concept of human sensitivity map. With the sensitivity map, we design a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. Experiments on real world data demonstrate the superior performance of the proposed model on human-imperceptible privacy protection.

Image sentiment influences visual perception. Emotion-eliciting stimuli such as happy faces and poisonous snakes are generally prioritized in human attention. However, little research has evaluated the interrelationships of image sentiment and visual saliency. In this paper, we present the first study to focus on the relation between emotional properties of an image and visual attention. We first create the EMOtional attention dataset (EMOd). It is a diverse set of emotion-eliciting images, and each image has (1) eye-tracking data collected from 16 subjects, (2) intensive image context labels including object contour, object sentiment, object semantic category, and high-level perceptual attributes such as image aesthetics and elicited emotions. We perform extensive analyses on EMOd to identify how image sentiment relates to human attention. We discover an emotion prioritization effect: for our images, emotion-eliciting content attracts human attention strongly, but such advantage diminishes dramatically after initial fixation. Aiming to model the human emotion prioritization computationally, we design a deep neural network for saliency prediction, which includes a novel subnetwork that learns the spatial and semantic context of the image scene. The proposed network outperforms the state-of-the-art on three benchmark datasets, by effectively capturing the relative importance of human attention within an image. The code, models, and dataset are available online at https://nus-sesame.top/emotionalattention/.

Visual realism is defined as the extent to which an image appears to people as a photo rather than computer generated. Assessing visual realism is important in applications like computer graphics rendering and photo retouching. However, current realism evaluation approaches use either labor-intensive human judgments or automated algorithms largely dependent on comparing renderings to reference images. We develop a reference-free computational framework for visual realism prediction to overcome these constraints. First, we construct a benchmark dataset of 2,520 images with comprehensive human annotated attributes. From statistical modeling on this data, we identify image attributes most relevant for visual realism. We propose both empirically-based (guided by our statistical modeling of human data) and deep convolutional neural network models to predict visual realism of images. Our framework has the following advantages: (1) it creates an interpretable and concise empirical model that characterizes human perception of visual realism; (2) it links computational features to latent factors of human image perception.

Automated assessment of visual sentiment has many applications, such as monitoring social media and facilitating online advertising. In current research on automated visual sentiment assessment, images are mainly input and processed as a whole. However, human attention is biased, and a focal region with high acuity can disproportionately influence visual sentiment. To investigate how attention influences visual sentiment, we conducted experiments that reveal critical insights into human perception. We discover that negative sentiments are elicited by the focal region without a notable influence of contextual information, whereas positive sentiments are influenced by both focal and contextual information. Building on these insights, we create new deep convolutional neural networks for sentiment prediction that have additional channels devoted to encoding focal information. On two benchmark datasets, the proposed models demonstrate superior performance compared with the state-of-the-art methods. Extensive visualizations and statistical analyses indicate that the focal channels are more effective on images with focal objects, especially for images that also elicit negative sentiments.