Results 11 to 20 of about 3,241,507 (337)
Super Jackstraws and Super Waterwheels [PDF]
Journal of High Energy Physics, 2006 We construct various new BPS states of D-branes preserving 8 supersymmetries. These include super Jackstraws (a bunch of scattered D- or (p,q)-strings preserving supersymmetries), and super waterwheels (a number of D2-branes intersecting at generic ...
A. Sen +12 more
core +3 more sources
Image Super-Resolution via Iterative Refinement [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al.
Chitwan Saharia +5 more
semanticscholar +1 more source
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Xintao Wang +3 more
semanticscholar +1 more source
Enhanced Deep Residual Networks for Single Image Super-Resolution [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Bee Lim +4 more
semanticscholar +1 more source
Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction [PDF]
Computer Vision and Pattern Recognition, 2021 We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.
Cheng Sun, Min Sun, Hwann-Tzong Chen
semanticscholar +1 more source
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
semanticscholar +1 more source
Activating More Pixels in Image Super-Resolution Transformer [PDF]
Computer Vision and Pattern Recognition, 2022 Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis.
Xiangyu Chen +3 more
semanticscholar +1 more source
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Computer Vision and Pattern Recognition, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi +7 more
semanticscholar +1 more source
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions ...
Yizhong Wang +39 more
semanticscholar +1 more source
Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
semanticscholar +1 more source