150 citations · 211 across the 18 of their papers we have counts for
5 papers · 1 filter
Source-free Depth for Object Pop-out
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan +5
Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promi…
RGB-Event Fusion for Moving Object Detection in Autonomous Driving
Zhuyun Zhou, Zongwei Wu, Rémi Boutteau +3
Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approa…
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…
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks
Daniel Braun, Olivier Morel, Pascal Vasseur +1
Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap…