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Found 19 papers, 14 papers with code
EBSR: Enhanced Binary Neural Network for Image Super-Resolution
While the performance of deep convolutional neural networks for image super-resolution (SR) has improved significantly, the rapid increase of memory and computation requirements hinders their deployment on resource-constrained devices.
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.
TCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
Ranked #1 on
Deblurring
on DVD
VRT: A Video Restoration Transformer
Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
Ranked #1 on
Deblurring
on BASED
Image Super-Resolution With Non-Local Sparse Attention
NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation.
SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment
In our system, we expand the number of external constraints in multiple languages to obtain more specialised multilingual word embeddings.
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.
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image.
Ranked #6 on
Image Super-Resolution
on Manga109 - 3x upscaling
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
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.
Scale-wise Convolution for Image Restoration
In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance.
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.
Video Instance Segmentation
The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos.
Ranked #37 on
Video Instance Segmentation
on YouTube-VIS validation
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
Ranked #16 on
Video Object Segmentation
on YouTube-VOS 2018
(F-Measure (Unseen) metric)
Wide Activation for Efficient and Accurate Image Super-Resolution
Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
Ranked #4 on
Multi-Frame Super-Resolution
on PROBA-V
Non-Local Recurrent Network for Image Restoration
The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.
Ranked #1 on
Grayscale Image Denoising
on Set12 sigma30
Robust Video Super-Resolution With Learned Temporal Dynamics
Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.
