activity
20132023
most citedGeneralized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

272 citations · 959 across the 34 of their papers we have counts for

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Showing 2023Show all

12 papers · 1 filter

cs.CV2023★ 1 cited

RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion

Zhiqiang Yan, Xiang Li, Le Hui +3

Depth completion aims to recover dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent depth methods primarily focus on image guided…

cs.CV2023

AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization

Kun Wang, Zhiqiang Yan, Huang Tian +4

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to t…

cs.CV2023★ 1 cited

Creative Birds: Self-Supervised Single-View 3D Style Transfer

Renke Wang, Guimin Que, Shuo Chen +3

In this paper, we propose a novel method for single-view 3D style transfer that generates a unique 3D object with both shape and texture transfer. Our focus lies primarily on birds…

cs.CV2023

Learnable Differencing Center for Nighttime Depth Perception

Zhiqiang Yan, Yupeng Zheng, Chongyi Li +2

Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth per…

cs.CV2023

Variable Radiance Field for Real-World Category-Specific Reconstruction from Single Image

Kun Wang, Zhiqiang Yan, Zhenyu Zhang +3

Reconstructing category-specific objects using Neural Radiance Field (NeRF) from a single image is a promising yet challenging task. Existing approaches predominantly rely on proje…

cs.CV2023★ 8 cited

Fine-Grained Visual Prompting

Lingfeng Yang, Yueze Wang, Xiang Li +2

Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited…