Traditional knowledge distillation relies on simple MSE or KL divergence losses that fail to capture the complex distributional relationships between teacher and student model representations. We propose FlowDistill, a novel distillation framework that employs normalizing flows to model and transfer the intricate knowledge distributions from teacher to student models. Our approach introduces three key innovations: (1) Invertible Knowledge Mapping using continuous normalizing flows (CNFs) to learn bijective transformations between teacher and student representation spaces, enabling precise knowledge transfer without information loss, (2) Flow-Guided Progressive Distillation that gradually increases the complexity of knowledge transfer by learning hierarchical flow transformations from simple to complex distributions, and (3) Conditional Flow Networks that adapt knowledge transfer based on input context and task requirements. Unlike previous diffusion-based distillation methods such as DiffKD that suffer from computational overhead due to iterative denoising processes and information loss during noise addition, our flow-based approach provides exact invertible transformations with significantly reduced computational cost. Extensive experiments on ImageNet classification, COCO object detection, and Cityscapes semantic segmentation demonstrate that FlowDistill achieves superior performance with 2.1% accuracy improvement over DiffKD on ResNet-34 to ResNet-18 distillation while reducing inference time by 3.5×. Our method establishes new state-of-the-art results across multiple distillation benchmarks and provides theoretical guarantees for lossless knowledge transfer through invertible flow transformations.

Interactive visual query systems are essential for the exploration and analysis of geospatial data. However, developing such systems has become increasingly challenging in recent years due to the conflict between the unprecedented volume of data and the need for instantaneous feedback. To address this challenge, we present HiVQ, a High-performance Visual Query system for real-time interactive visual query of geospatial big data. HiVQ adopts an innovative “Query as Visualization” paradigm, transforming user interactions into pixel value queries which can be processed efficiently with specialized indices and optimization strategies. Unlike conventional solutions that query and visualize geospatial objects sequentially, HiVQ effectively omits most geospatial objects and unnecessary computations that do not affect the final visualization, ensuring minimal sensitivity to data volume. Experimental results show that HiVQ accelerates visual queries by at least seven times compared to SOTA methods. This demonstration enables users to interactively explore and analyze spatial data with billions of nodes at any scale, receiving responses in milliseconds as they dynamically adjust analysis parameters, query conditions, or map styling. The demonstration video is available at https://gitee.com/kyrie-Bang/HiVQ-Demo.

Accurate classification of Movement Impairment (MI) and Motor Control Impairment (MCI) in non-specific low back pain (NSLBP) is essential for targeted rehabilitation but remains challenging due to subjective assessments and subtle movement differences. We present MOVEXor, a lightweight and explainable multi-modal framework that integrates spinal curvature images and motion-derived features through a modality-aware attention gating mechanism. MOVEXor achieves high classification performance (up to 97.5% accuracy) while offering transparent decision-making via Grad-CAM and Integrated Gradients (IG). Our analysis shows that the model focuses on physiologically meaningful movement phases, particularly minimal flexion angle, and relies heavily on motion stability for classification. The fused attention-based design outperforms static fusion methods, especially when handling noisy inputs. With minimal hardware requirements and real-time explainability, MOVEXor holds strong potential as a clinical decision-support tool for both in-clinic and remote settings, enabling objective, interpretable, and personalised rehabilitation exercise of LBP subgroups.

Low Back Pain (LBP) is a prevalent musculoskeletal disorder affecting over 80% of the population over their lifetime and is a leading cause of disability globally. The most frequent type, non-specific LBP (NSLBP) does not have a clearly identifiable pathology cause. Current clinical guidelines advocate for tailored management and self-care approaches for NSLBP. The effectiveness of these personalised management plans significantly depends on accurate and on-going assessment of the patient’s spinal function. This presents considerable challenges for both clinicians and patients. This study introduces “SpineSighter”, an artificial intelligence (AI) model developed to tailor management of NSLBP by categorising patients based on their spinal function either into High Function (HF) and Low Function (LF) subsets. Utilising standard video recordings and computer vision technology, SpineSighter analyses motion features such as angular displacement, velocity, and acceleration during repeated forward flexion tests. The model showed high accuracy in classifying spinal function, achieving an accuracy of 95.13%, sensitivity of 93.81%, specificity of 96.00%, and an F1 score of 0.9442. This innovative use of AI highlights the importance of velocity as a critical indicator of spinal functional differences, opening new avenues for personalised clinical management, self-care and recovery strategies of NSLBP.