332 citations · 994 across the 17 of their papers we have counts for
6 papers · 1 filter
Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang +2
Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be…
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
Pengwei Liang, Junjun Jiang, Xianming Liu +1
The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kern…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Zhiwei Zhong, Xianming Liu, Junjun Jiang +3
Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grad…
Omniscient Video Super-Resolution
Peng Yi, Zhongyuan Wang, Kui Jiang +4
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previousl…
Deep Learning-based Face Super-Resolution: A Survey
Junjun Jiang, Chenyang Wang, Xianming Liu +1
Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face i…