11 citations · 14 across the 10 of their papers we have counts for
10 papers
Addressing Imbalance for Class Incremental Learning in Medical Image Classification
Xuze Hao, Wenqian Ni, Xuhao Jiang +2
Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneous…
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation
Ri Cheng, Ruian He, Xuhao Jiang +3
Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An eff…
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior
Shili Zhou, Xuhao Jiang, Weimin Tan +2
In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider…
Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation
Ri Cheng, Xuhao Jiang, Ruian He +3
The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spat…
Multi-Modality Deep Network for JPEG Artifacts Reduction
Xuhao Jiang, Weimin Tan, Qing Lin +3
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for…
Multi-Modality Deep Network for Extreme Learned Image Compression
Xuhao Jiang, Weimin Tan, Tian Tan +2
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur…
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Maurizio Denna +93
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Xuhao Jiang, Weimin Tan, Ri Cheng +2
Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorpor…
Perception-Oriented Stereo Image Super-Resolution
Chenxi Ma, Bo Yan, Weimin Tan +1
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on im…
Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks
Xuhao Jiang, Liquan Shen, Guorui Feng +2
In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference and no-reference screen content image (SCI) q…