61 citations · 352 across the 38 of their papers we have counts for
14 papers · 1 filter
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…
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
Shichao Dong, Jin Wang, Renhe Ji +3
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by…
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
Ziwei Luo, Youwei Li, Lei Yu +4
Deep learning-based single image super-resolution (SISR) approaches have drawn much attention and achieved remarkable success on modern advanced GPUs. However, most state-of-the-ar…
Efficient One Pass Self-distillation with Zipf's Label Smoothing
Jiajun Liang, Linze Li, Zhaodong Bing +4
Self-distillation exploits non-uniform soft supervision from itself during training and improves performance without any runtime cost. However, the overhead during training is ofte…
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Yunhui Han, Kunming Luo, Ao Luo +4
Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing appr…
Explaining Deepfake Detection by Analysing Image Matching
Shichao Dong, Jin Wang, Jiajun Liang +2
This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspecti…
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Ziwei Luo, Youwei Li, Shen Cheng +6
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-…
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Yisheng He, Haoqiang Fan, Haibin Huang +2
In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstra…
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
Yisheng He, Yao Wang, Haoqiang Fan +2
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity…
Deep Constrained Least Squares for Blind Image Super-Resolution
Ziwei Luo, Haibin Huang, Lei Yu +3
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules. Following the common practices of blind SR, ou…
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
Jiankun Li, Peisen Wang, Pengfei Xiong +6
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract d…
Learning Optical Flow with Adaptive Graph Reasoning
Ao Luo, Fan Yang, Kunming Luo +3
Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques l…