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Found 12 papers, 2 papers with code
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Sequenced-Replacement Sampling for Deep Learning
We propose sequenced-replacement sampling (SRS) for training deep neural networks.
Gradient-Coherent Strong Regularization for Deep Neural Networks
However, we observe that imposing strong L1 or L2 regularization with stochastic gradient descent on deep neural networks easily fails, which limits the generalization ability of the underlying neural networks.
Achieving Strong Regularization for Deep Neural Networks
L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions.
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
In this paper, we propose a Residual Channel Attention-Generative Adversarial Network(RCA-GAN) to solve these problems.
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition
Empirical results confirm the efficiency and effectiveness of residual frames as well as the proposed pseudo-3D convolution module.
GIA-Net: Global Information Aware Network for Low-light Imaging
In this paper, we propose a global information aware (GIA) module, which is capable of extracting and integrating the global information into the network to improve the performance of low-light imaging.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection
Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors.
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition
This paper presents a pure transformer-based approach, dubbed the Multi-Modal Video Transformer (MM-ViT), for video action recognition.
Real-Time Super-Resolution for Real-World Images on Mobile Devices
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts.
