298 citations · 1.5k across the 72 of their papers we have counts for
12 papers · 1 filter
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Zhenyu Li, Zehui Chen, Jialei Xu +2
Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationa…
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
Yuanchao Bai, Xianming Liu, Kai Wang +3
Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and…
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai +3
The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resu…
Learning Towards the Largest Margins
Xiong Zhou, Xianming Liu, Deming Zhai +3
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. Th…
Towards Model Generalization for Monocular 3D Object Detection
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved tremendous improvements with emerging large-scale autonomous driving datasets and the rapid development of deep learning techniq…
GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting
Zeyu Lu, Junjun Jiang, Junqin Huang +2
The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional…
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Chenyang Wang, Junjun Jiang, Xiong Zhou +1
Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet th…
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However,…
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Wenbo Zhao, Xianming Liu, Zhiwei Zhong +4
Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised l…
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
Zhenyu Li, Zehui Chen, Xianming Liu +1
This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential…
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to…
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
Zhenyu Li, Zehui Chen, Ang Li +6
Pre-training has become a standard paradigm in many computer vision tasks. However, most of the methods are generally designed on the RGB image domain. Due to the discrepancy betwe…