activity
20172023
most citedGeneralized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

272 citations · 720 across the 17 of their papers we have counts for

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24 papers · 1 filter

cs.CV2023

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.CV20231 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.CV20222 cited

DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion

Zhiqiang Yan, Kun Wang, Xiang Li +3

Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually s…

cs.CV202236 cited

Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality

Xiang Li, Wenhai Wang, Lingfeng Yang +1

Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-tra…

cs.CV202210 cited

RecursiveMix: Mixed Learning with History

Lingfeng Yang, Xiang Li, Borui Zhao +2

Mix-based augmentation has been proven fundamental to the generalization of deep vision models. However, current augmentations only mix samples at the current data batch during tra…