150 citations · 157 across the 9 of their papers we have counts for
6 papers · 1 filter
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
Yiting Wang, Nolwenn Peyratout, Tim Brodermann +13
This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants…
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau +2
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods e…
Robust RGB-D Fusion for Saliency Detection
Zongwei Wu, Shriarulmozhivarman Gobichettipalayam, Brahim Tamadazte +3
Efficiently exploiting multi-modal inputs for accurate RGB-D saliency detection is a topic of high interest. Most existing works leverage cross-modal interactions to fuse the two s…
Depth-Adapted CNNs for RGB-D Semantic Segmentation
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side. Existing works often adopt a two-str…
Modality-Guided Subnetwork for Salient Object Detection
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the…
Depth-Adapted CNN for RGB-D cameras
Zongwei Wu, Guillaume Allibert, Christophe Stolz +1
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the ph…