150 citations · 165 across the 11 of their papers we have counts for
12 papers · 1 filter
NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos
Ziyu Yang, Sucheng Ren, Zongwei Wu +4
Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies. This work aims to take a step forward to understand how humans perc…
Alignment-free HDR Deghosting with Semantics Consistent Transformer
Steven Tel, Zongwei Wu, Yulun Zhang +4
High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual informa…
Object Segmentation by Mining Cross-Modal Semantics
Zongwei Wu, Jingjing Wang, Zhuyun Zhou +5
Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy.…
Event-Free Moving Object Segmentation from Moving Ego Vehicle
Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel +5
Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving, especially for sequences obtained from mo…
CamDiff: Camouflage Image Augmentation via Diffusion Model
Xue-Jing Luo, Shuo Wang, Zongwei Wu +4
The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we hav…
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…
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…
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…