In sports analytics, tactical visualization is widely used to convey valuable insights. However, due to the complex domain knowledge and contextual information involved in tactical visualizations, it is challenging for users to connect high-level tactical insights to corresponding visual patterns. This requires users to engage in a reasoning process to interpret insights within game contexts, which remains insufficiently supported in existing visual-text linking studies. In this work, we propose InsightChaser, a novel approach to bridge tactical insights and soccer visualizations through visual-text linking and visual reasoning enhancement. InsightChaser constructs knowledge graphs to represent both visual elements and contextual game information. Integrating large language models (LLMs), our approach retrieves relevant visual elements and establishes explicit links with insights. Moreover, InsightChaser utilizes LLMs to enhance these visual-text links by providing reasoning explanations and visual effects. We further develop an interactive visualization system that supports navigation and explanation of enhanced visual-text links. Users can explore linked tactical insights interactively and reason through enhanced visual explanations. We conduct two case studies using real-world soccer data and a user study to demonstrate the effectiveness of our approach.

In basketball, decision-making is one of the core skills for players. For example, when a player is holding the ball, the success of the team’s offense is primarily determined by her/his decisions (i.e., pass, shoot, or dribble) in response to the dynamics of the game. Understanding players’ decision-making processes in changing game situations can help coaches develop effective strategies, which is critical for the success of a team. However, the decision-making process is influenced by various factors (e.g., player’s playing style, opponents’ defense, and time remaining), making understanding a challenging problem. In this study, we propose HoopScouter, a visual analytics system to help understand ball handlers’ decisions in basketball games. Based on a careful investigation of the analysis requirements, we first introduce a representation learning method that characterizes ball handlers’ decision-making styles. We then design a sketch panel with integrated time information to support exploration of player decisions under similar game scenarios. Facet views and coordinated interactions are also provided to identify the strengths and weaknesses of the ball handler’s decision-making, and to understand when and why ball handlers would make certain decisions. To validate the effectiveness of HoopScouter, we conduct two case studies on real-world basketball games and receive positive feedback from domain experts.

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

This paper presents a multi-scale mask cross-layer fusion network (MMCF) for industrial surface defect detection. MMCF exploits the transformer’s ability to preserve global information, but replaces the large-kernel large-stride convolution in the patches embedding with a small-kernel small-stride convolution stacking to reduce redundancy. Moreover, MMCF uses different scale complementarily masks to occlude normal images and employs an encoder-decoder structure with cross-layer feature fusion to reconstruct the occluded regions. After smoothing the complementary reconstructed parts using pixel attention, merge them together to form a complete reconstructed image. The anomaly score is computed based on the gradient magnitude similarity between the input image and the reconstruction image. Experiments on the MVTec AD dataset with 15 product categories show that MMCF outperforms other surface defect detection methods in terms of AUC values.

The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions. Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity, and have difficulty in generating realistic and multi-pattern mollusk motions. In this work, we present a large-scale dynamic pose dataset of Drosophila larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path. The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method. Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance. Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.