6 papers
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Siyu Li, Fei Teng, Yihong Cao +3
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, a…
Panoramic Out-of-Distribution Segmentation
Mengfei Duan, Yuheng Zhang, Yihong Cao +5
Panoramic imaging enables capturing 360° images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception, which is critical to applications, such as autonomous…
Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
Yihong Cao, Jiaming Zhang, Xu Zheng +5
Panoramic image processing is essential for omni-context perception, yet faces constraints like distortions, perspective occlusions, and limited annotations. Previous unsupervised…
Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection
Hongda Qin, Xiao Lu, Zhiyong Wei +3
Generalizing an object detector trained on a single domain to multiple unseen domains is a challenging task. Existing methods typically introduce image or feature augmentation to d…
MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation
Chenfei Liao, Xu Zheng, Yuanhuiyi Lyu +5
Research has focused on Multi-Modal Semantic Segmentation (MMSS), where pixel-wise predictions are derived from multiple visual modalities captured by diverse sensors. Recently, th…
HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors
Siyu Li, Yihong Cao, Hao Shi +4
The exploration of Bird's-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be…