3 accepted papers
"Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of semantic segmentation yet remains less explored to date. Recent endeavors regard RGB modality as the center and the others as the auxiliary, yielding an asymmetric architecture with two branches. However, t…
"Image modality is not perfect as it often fails in certain conditions, , night and fast motion. This significantly limits the robustness and versatility of existing multi-modal (, Image+X) semantic segmentation methods when confronting modality absence or failure, as often occurred in real-world ap…
"Reconstructing intensity frames from event data while maintaining high temporal resolution and dynamic range is crucial for bridging the gap between event-based and frame-based computer vision. Previous approaches have depended on supervised learning on synthetic data, which lacks interpretability…