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Found 42 papers, 24 papers with code
Geometric Estimation via Robust Subspace Recovery
Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques.
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Therefore, Diff-Retinex formulates the low-light image enhancement problem into Retinex decomposition and conditional image generation.
ResMatch: Residual Attention Learning for Local Feature Matching
In order to facilitate the learning of matching and filtering, we inject the similarity of descriptors and relative positions into cross- and self-attention score, respectively.
Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network
Then, our Matchable Keypoint-Assisted Context Aggregation Module regards sampled informative keypoints as message bottlenecks and thus constrains each keypoint only to retrieve favorable contextual information from intra- and inter- matchable keypoints, evading the interference of irrelevant and redundant connectivity with non-repeatable ones.
Image Deblurring by Exploring In-depth Properties of Transformer
We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks.
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models
In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process.
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object Detection
We find that context information from the long-term frame and temporal information from the short-term frame are two useful cues for video small object detection.
Sparsely Annotated Semantic Segmentation With Adaptive Gaussian Mixtures
Our AGMM can effectively endow reliable supervision for unlabeled pixels based on the distributions of labeled and unlabeled pixels.
Robust and Scalable Gaussian Process Regression and Its Applications
This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers.
Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications.
ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning
Deep-learning-based local feature extraction algorithms that combine detection and description have made significant progress in visible image matching.
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
RFNet: Unsupervised Network for Mutually Reinforcing Multi-Modal Image Registration and Fusion
Specifically, for image registration, we solve the bottlenecks of defining registration metrics applicable for multi-modal images and facilitating the network convergence.
MS2DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph
However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well.
Coherent Point Drift Revisited for Non-Rigid Shape Matching and Registration
In this paper, we explore a new type of extrinsic method to directly align two geometric shapes with point-to-point correspondences in ambient space by recovering a deformation, which allows more continuous and smooth maps to be obtained.
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Learning-based methods usually use a convolutional neural network (CNN) to learn the implicit priors of HSIs.
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e. g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details.
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (\emph{from less to more}) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk.
SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning
Modifications on triplet loss that rescale the back-propagated gradients of special pairs have made significant progress on local descriptor learning.
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.
Ranked #6 on
Image Defocus Deblurring
on DPD (Dual-view)
Pan-sharpening via High-pass Modification Convolutional Neural Network
Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification block.
Omniscient Video Super-Resolution
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.
Bilateral attention decoder: A lightweight decoder for real-time semantic segmentation
To verify the efficiency of the proposed bilateral attention decoder, we adopt a lightweight network as the backbone and compare our proposed method with other state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets.
Deep Learning-based Face Super-Resolution: A Survey
Second, we elaborate on the facial characteristics and popular datasets used in FSR.
Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd Counting
Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk.
T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning
We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning.
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL).
Multi-Scale Progressive Fusion Network for Single Image Deraining
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.
Ranked #6 on
Single Image Deraining
on Test2800
LaFIn: Generative Landmark Guided Face Inpainting
It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.
EDIT: Exemplar-Domain Aware Image-to-Image Translation
The principle behind is that, for images from multiple domains, the content features can be obtained by a uniform extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars).
Ensemble Super-Resolution with A Reference Dataset
In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers).
PFLD: A Practical Facial Landmark Detector
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.
Hyperspectral Image Classification in the Presence of Noisy Labels
The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.
Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning
To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL).
Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination
Most of the current face hallucination methods, whether they are shallow learning-based or deep learning-based, all try to learn a relationship model between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a training set.
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
(1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks.
Ranked #78 on
Semantic Segmentation
on NYU Depth v2
Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples
This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e. g., different lighting conditions, different glasses).
Density-Based Region Search with Arbitrary Shape for Object Localization
Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score.
Robust Estimation of Nonrigid Transformation for Point Set Registration
In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.
