U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

Abstract Knowledge distillation (KD) has witnessed its powerful capability in learning
compact models in object detection. Previous KD methods for object detection mostly focus …

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

Domain Adaptive Object Detection (DAOD) generalizes the object detector from an
annotated domain to a label-free novel one. Recent works estimate prototypes (class …

The advent of 3D Gaussian Splatting (3D-GS) techniques and their dynamic scene modeling
variants, 4D-GS, offers promising prospects for real-time rendering of dynamic surgical …

Object detection and depth perception are key foundations of object tracking and machine
navigation, facilitating a thorough perception and understanding of the surrounding …

Object detection is of great importance for remote sensing image interpretation work and has
received significant attention. However, small weak object detection has always been a …

Abstract Domain Adaptive Object Detection (DAOD) transfers an object detector to a novel
domain free of labels. However, in the real world, besides encountering novel scenes, novel …

Generating diverse plausible outputs from a single input is crucial for addressing visual
ambiguities, exemplified in medical imaging where experts may provide varying semantic …

Domain Adaptive Object Detection (DAOD) transfers an object detector from the labeled
source domain to a novel unlabelled target domain. Recent advances bridge the domain …