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Found 28 papers, 19 papers with code
LatticeNet: Towards Lightweight Image Super-resolution with Lattice Block
Drawing lessons from lattice filter bank, we design the lattice block (LB) in which two butterfly structures are applied to combine two RBs.
A New Backbone for Hyperspectral Image Reconstruction
The study of 3D hyperspectral image (HSI) reconstruction refers to the inverse process of snapshot compressive imaging, during which the optical system, e. g., the coded aperture snapshot spectral imaging (CASSI) system, captures the 3D spatial-spectral signal and encodes it to a 2D measurement.
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks.
Adapting Stepsizes by Momentumized Gradients Improves Optimization and Generalization
Adaptive gradient methods, such as \textsc{Adam}, have achieved tremendous success in machine learning.
On the Importance of Environments in Human-Robot Coordination
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.
Pseudo 3D Auto-Correlation Network for Real Image Denoising
The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain.
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network
They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors.
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR).
Space-time Video Super-resolution
Video Frame Interpolation
+1
Emerging Paradigms of Neural Network Pruning
In spite of the encouraging progress, how to coordinate these new pruning fashions with the traditional pruning has not been explored yet.
GAN Inversion: A Survey
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator.
Neural Pruning via Growing Regularization
Regularization has long been utilized to learn sparsity in deep neural network pruning.
Neural Sparse Representation for Image Restoration
Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.
Pyramid Attention Networks for Image Restoration
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
Adversarial Feature Hallucination Networks for Few-Shot Learning
The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data.
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.
Ranked #1 on
Video Frame Interpolation
on Vid4 - 4x upscaling
Space-time Video Super-resolution
Video Frame Interpolation
+1
Texture Hallucination for Large-Factor Painting Super-Resolution
We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e. g., 8X, 16X).
Joint Super-Resolution and Alignment of Tiny Faces
As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
Visual Semantic Reasoning for Image-Text Matching
It outperforms the current best method by 6. 8% relatively for image retrieval and 4. 8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set).
Ranked #5 on
Image Retrieval
on Flickr30K 1K test
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale.
Multimodal Style Transfer via Graph Cuts
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Residual Dense Network for Image Restoration
We fully exploit the hierarchical features from all the convolutional layers.
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames).
Channel Splitting Network for Single MR Image Super-Resolution
The proposed CSN model divides the hierarchical features into two branches, i. e., residual branch and dense branch, with different information transmissions.
Support Neighbor Loss for Person Re-Identification
To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #10 on
Image Super-Resolution
on Set5 - 4x upscaling
Residual Dense Network for Image Super-Resolution
In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.
Ranked #1 on
Color Image Denoising
on CBSD68 sigma50