A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper.

In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players’ decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players’ spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players’ relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players’ distances significantly affects the prediction accuracy.

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods.

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.

There are various sentiment theories for categorizing human sentiments into several discrete sentiment categories, which means that the theory used for training sentiment prediction methods does not always match that used in the test phase. As a solution to this problem, zero-shot visual sentiment prediction methods have been proposed to predict unseen sentiments for which no images are available in the training phase. However, the training of these previous zero-shot methods relies on a single sentiment theory, which limits their ability to handle sentiments from other theories. Thus, this paper proposes a more robust zero-shot visual sentiment prediction method that can handle cross-domain sentiments defined in different sentiment theories. Specifically, by focusing on the fact that sentiments are abstract concepts common to humans regardless of whether their theories are different, we incorporate knowledge distillation into our method to construct a teacher–student model that can train the implicit relationships between sentiments defined in different sentiment theories. Furthermore, to enhance sentiment discrimination capability and strengthen the implicit relationships between sentiments, we introduce a novel sentiment loss between the teacher and student models. In this way, our model becomes robust to unseen sentiments by exploiting the implicit relationships between sentiments. The contributions of this paper are the introduction of knowledge distillation and a novel sentiment loss between the teacher and student models for zero-shot visual sentiment prediction, and improved performance of zero-shot visual sentiment prediction. Experiments on several open datasets demonstrate the effectiveness of the proposed method.