13 accepted papers
6D pose estimation of textureless objects is valu- able for industrial robotic applications, yet remains challenging due to the frequent loss of depth information. Current multi-view methods either rely on depth data or insufficiently exploit multi-view geometric cues, limiting their performance. In…
Cognitive diagnosis (CD), inferring student knowledge mastery based on historical response records, is crucial for personalized educational services such as adaptive practice and learning path planning. Existing CD models were built based on the assumption that student
Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emphasize general knowledge and vertical step-by-step reasoning typical of STEM disciplines, while overlooking the distinct…
With the rapid advancement of intelligent education, Computerized Adaptive Testing (CAT) has attracted increasing attention by integrating educational psychology with deep learning technologies. Unlike traditional paper-and-pencil testing, CAT aims to efficiently and accurately assess ex- aminee abi
Cognitive diagnosis is crucial for intelligent education because of its ability to reveal students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diag
Computerized adaptive testing(CAT) is a crucial task in computer-aided education, which aims to adaptively select suitable question to diagnose examinees' ability status. Existing CAT approaches enhance selection performance by exploring examinee-question(E-Q) relation. These approaches either exclu…
Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementatio…
Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in b…
Contrastive learning has proven to be highly efficient and adaptable in shaping representation spaces across diverse modalities by pulling similar samples together and pushing dissimilar ones apart. However, two key limitations persist: (1) Without explicit regulation of the embedding distribution,…
Robotic breast ultrasound (RBUS) aims to standardize breast ultrasonography, reduce the workload of sonographers, and provide high-quality ultrasound (US) images for subsequent diagnosis. In the process of RBUS screening, adjusting the US probe correctly and efficiently to acquire high-quality US im
Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poor…
Autonomous ultrasound scanning robots have attracted the attention of researchers, and the real-time quality assessment of ultrasound images is the key technology of them. Existing robot systems usually use pixel-level feature statistical methods such as grayscale, confidence map, etc. However, in c
Natural orifice transluminal endoscopic surgery (NOTES) has received significant attentions due to its minimal incision trauma compared with traditional multi-port robot assisted surgery. Continuum robot can be used in NOTES due to its high flexibility which can adapt to circuitous paths. However, t