Event-based object detection plays a crucial role in scenarios involving high-speed motion, extreme lighting conditions, and high-frequency detection. However, existing methods fail to address the challenges posed by small objects, including discriminative feature deficiency, the loss of critical information, and the inherent sparsity of event data. Moreover, the lack of benchmark datasets has significantly hindered progress in this field. To tackle these issues, we propose the Fully Deformable Detection Network (FDDNet), a lightweight framework that dynamically adapts to extract key features. First, we introduce a Long-Term Deformable Temporal Receptive Module (LDTR), which aligns critical features across consecutive event streams and leverages a State Space Model for long-range temporal modeling, enhancing the detection of high-speed small objects. Second, to address the sparsity of event data and the concentration of key features along object edges, we design a Sparse Feature Aggregation Block (SFAB) within the backbone and a coarse-to-fine deformable detection head, enabling hierarchical feature refinement from local to global, and improving the detection quality of sparse targets. Finally, to mitigate the lack of event-based small object datasets, we develop a high-quality, annotation-free data acquisition method and collect a real-world benchmark dataset for validation. Extensive experiments demonstrate that our approach achieves state-of-the-art (SOTA) performance on event-based small object detection tasks, with a mAP of 37.4% (+2.4%) on our benchmark and runs at 88 FPS, showcasing both accuracy and real-time capability. Our code and Supplement are available at https://github.com/Lqm26/ESOD.

Abstract There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known‐item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.

Information representation is the core problem in an information searching system. Only if information resources are represented at an appropriate level that matches the ability of user groups can the success and efficiency of information searching be realized. Information systems for young children frequently ignore the problem of information representation, which renders the use of such systems difficult for young children and parents. This study focused on young children's picture book searching demand and carried out behavioral experiments by simulating an information searching system to extract young children's information representation of picture books. The findings include 8 representation categories and 19 elements that children use for searching picture books, which may contribute to the development of a picture book searching system for young children.