activity
20172022
most citedGeneralized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

272 citations · 719 across the 16 of their papers we have counts for

collaborators

26 papers

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…

cs.CV20221 cited

Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information

Lingfeng Yang, Xiang Li, Renjie Song +5

Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on vi…

cs.CV2021

Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation

Zheng Li, Xiang Li, Lingfeng Yang +2

Knowledge distillation usually transfers the knowledge from a pre-trained cumbersome teacher network to a compact student network, which follows the classical teacher-teaching-stud…

cs.CV20215 cited

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark

Kun Wang, Zhenyu Zhang, Zhiqiang Yan +4

Monocular depth estimation aims at predicting depth from a single image or video. Recently, self-supervised methods draw much attention since they are free of depth annotations and…