Search Results for author:
Found 10 papers, 1 papers with code
Addressing Imbalance for Class Incremental Learning in Medical Image Classification
Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneously available.
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation
In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample.
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior
Motion vectors, one of the compression information, record the motion of the video frames.
Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation
We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision.
Multi-Modality Deep Network for JPEG Artifacts Reduction
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress.
Multi-Modality Deep Network for Extreme Learned Image Compression
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view.
Perception-Oriented Stereo Image Super-Resolution
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR.
Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks
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) quality assessment.
Full reference image quality assessment
Full-Reference Image Quality Assessment
+2
