4 accepted papers
Domain Generalized Semantic Segmentation (DGSS) trains a model on a labeled source domain to generalize to unseen target domains with consistent contextual distribution and varying visual appearance. Most existing methods rely on domain randomization or data generation but struggle to capture the un…
Recently, open-vocabulary semantic segmentation has garnered growing attention. Most current methods leverage vision-language models like CLIP to recognize unseen categories through their zero-shot capabilities. However, CLIP struggles to establish potential spatial dependencies among scene objects…
Enhancing the cross-domain generalization of 3D semantic segmentation is a pivotal task in computer vision that has recently gained increasing attention. Most existing methods, whether using consistency regularization or cross-modal feature fusion, focus solely on individual objects while overlookin…
Despite its remarkable performance, RGB-based Multi-human Pose Estimation (MPE) technology has many practical limitations, such as nighttime and smoggy environments. Infrared imaging is a valid substitution in these scenarios but needs an efficient and fast method for MPE. This letter aims to design