2 citations · 11 across the 9 of their papers we have counts for
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Multi-Order Matching Network for Alignment-Free Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Yuan Wu +3
Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-quality depth reconstruction. However,…
SpatioTemporal Difference Network for Video Depth Super-Resolution
Zhengxue Wang, Yuan Wu, Xiang Li +2
Depth super-resolution has achieved impressive performance, and the incorporation of multi-frame information further enhances reconstruction quality. Nevertheless, statistical anal…
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Yuan Wu, Zhiqiang Yan, Yigong Zhang +2
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on…
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
Yuan Wu, Zhiqiang Yan, Zhengxue Wang +3
The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is a…
Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention
Yimian Dai, Peiwen Pan, Yulei Qian +4
Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detec…
OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation
Kepan Nan, Rui Xie, Penghao Zhou +6
Text-to-video (T2V) generation has recently garnered significant attention thanks to the large multi-modality model Sora. However, T2V generation still faces two important challeng…
DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images
Yimian Dai, Minrui Zou, Yuxuan Li +3
Synthetic Aperture Radar (SAR) target detection has long been impeded by inherent speckle noise and the prevalence of diminutive, ambiguous targets. While deep neural networks have…
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
Zheng Li, Xiang Li, Xinyi Fu +4
Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses o…