6 accepted papers
Transformable wheel-legged robots can adjust their configuration according to terrain conditions, enabling effective operation in harsh environments. While existing controllers based on preset commands have successfully demonstrated the feasibility of reconfigurable mechanisms, they still struggle t…
Images captured under severe weather conditions often suffer from complex, composite degradations, varying in intensity. In this paper, we introduce a novel method, Dual-Level Prototype Learning (DPL), to tackle the challenging task of composite degraded image restoration. Unlike previous methods th…
Smoke segmentation is of great importance in precisely identifying the smoke location, enabling timely fire rescue and gas leak detection. However, due to the visual diversity and blurry edges of the non-grid smoke, noisy labels are almost inevitable in large-scale pixel-level smoke datasets. Noisy…
"Early smoke segmentation (ESS) plays a crucial role in accurately locating the source of smoke, facilitating prompt fire rescue operations and gas leak detection. Unlike regular objects, which are typically rigid, opaque, and have clear boundaries, ESS presents challenges due to the large areas of…
Early smoke segmentation (ESS) enables the accurate identification of smoke sources, facilitating the prompt extinguishing of fires and preventing large-scale gas leaks. But ESS poses greater challenges than conventional object and regular smoke segmentation due to its small scale and transparent ap…
Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption…