Recent self-supervised pre-training methods for object detection often rely on generic object proposals for localization and semantic feature learning for classification, but they yield limited improvements when applied to Detection Transformers (DETR) due to a lack of architectural alignment. Hence, we propose an elegant and versatile self-supervised framework tailored for DETR-like models called Distance-aware Multi-view Contrastive Learning (DisCo DETR). DisCo DETR enhances localization and semantic features through two core components. (i) Distance-aware Multi-view Object Query Fusion explicitly guides object queries to focus on spatially close objects across views, stabilizing training and improving localization accuracy. (ii) Contrastive Learning for DETR uses native bipartite matching to identify positive output pairs across views and pull them closer, enhancing semantic features discrimination with no extra matching. DisCo DETR can be seamlessly integrated into DETR-like models and achieves SOTA transfer performance on PASCAL VOC and COCO benchmarks across multiple variants.

Children with cerebral palsy (CP) can experience complex gait deviations and need to go through intensive lower extremity rehabilitation exercises to develop and enhance their motor control in daily living. However, most of them cannot persist in the regular repetitive exercise sessions using hospital-based equipment. To provide a playful and attractive rehabilitation environment, an interactive carpet with interchangeable covers and varied step lengths is introduced to motivate children for lower extremity training. The vibrant colours, engaging games, visual and audio feedback are designed to increase the carpet-human interaction. This carpet can support gait exercise with five types of step lengths, which improves its accessibility and usability for children with CP.