Search Results for author:
Found 69 papers, 28 papers with code
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.
CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation
Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video.
Ranked #34 on
Video Instance Segmentation
on YouTube-VIS validation
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.
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 #7 on
Image Super-Resolution
on Manga109 - 3x upscaling
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.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Laplacian Denoising Autoencoder
This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
Deep Affinity Net: Instance Segmentation via Affinity
Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances; and keypoint-based approaches in which individual instances are represented by a set of keypoints followed by a dense pixel clustering around those keypoints.
AlignSeg: Feature-Aligned Segmentation Networks
Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
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.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.
Ranked #3 on
Instance Segmentation
on Cityscapes test
(using extra training data)
Any-Precision Deep Neural Networks
When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision.
Panoptic-DeepLab
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.
One-to-one Mapping for Unpaired Image-to-image Translation
Here we propose a self-inverse network learning approach for unpaired image-to-image translation.
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space.
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Ranked #2 on
Pose Estimation
on UAV-Human
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process.
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity
In this paper, we investigate the role of Rademacher complexity in improving generalization of DNNs and propose a novel regularizer rooted in Local Rademacher Complexity (LRC).
CCNet: Criss-Cross Attention for Semantic Segmentation
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #7 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.
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
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
Despite remarkable progress, weakly supervised segmentation methods are still inferior to their fully supervised counterparts.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation.
Image Super-Resolution via Dual-State Recurrent Networks
Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks.
Ranked #41 on
Image Super-Resolution
on BSD100 - 4x upscaling
Generative Image Inpainting with Contextual Attention
Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
Enhance Visual Recognition under Adverse Conditions via Deep Networks
Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage.
Dilated Recurrent Neural Networks
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
Ranked #24 on
Sequential Image Classification
on Sequential MNIST
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.
On the Suboptimality of Proximal Gradient Descent for $\ell^{0}$ Sparse Approximation
We study the proximal gradient descent (PGD) method for $\ell^{0}$ sparse approximation problem as well as its accelerated optimization with randomized algorithms in this paper.
Discriminative Similarity for Clustering and Semi-Supervised Learning
By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier.
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision.
Fast Generation for Convolutional Autoregressive Models
In this work, we describe a method to speed up generation in convolutional autoregressive models.
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective.
Fast Wavenet Generation Algorithm
This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet.
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters
This paper emphasizes the significance to jointly exploit the problem structure and the parameter structure, in the context of deep modeling.
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".
Streaming Recommender Systems
The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.
D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images.
Learning A Deep $\ell_\infty$ Encoder for Hashing
We investigate the $\ell_\infty$-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning.
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance.
Brain-Inspired Deep Networks for Image Aesthetics Assessment
Image aesthetics assessment has been challenging due to its subjective nature.
Studying Very Low Resolution Recognition Using Deep Networks
Visual recognition research often assumes a sufficient resolution of the region of interest (ROI).
$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images.
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.
Learning with $\ell^{0}$-Graph: $\ell^{0}$-Induced Sparse Subspace Clustering
Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC) \cite{ElhamifarV13} and $\ell^{1}$-graph \cite{YanW09, ChengYYFH10}, are effective in partitioning the data that lie in a union of subspaces.
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn.
Learning Deep $\ell_0$ Encoders
We study the $\ell_0$ sparse approximation problem with the tool of deep learning, by proposing Deep $\ell_0$ Encoders.
Learning A Task-Specific Deep Architecture For Clustering
In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both.
DeepFont: Identify Your Font from An Image
As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers.
Ranked #1 on
Font Recognition
on VFR-Wild
Understanding Image Structure via Hierarchical Shape Parsing
A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.
Self-Tuned Deep Super Resolution
Deep learning has been successfully applied to image super resolution (SR).
Real-World Font Recognition Using Deep Network and Domain Adaptation
We address a challenging fine-grain classification problem: recognizing a font style from an image of text.
Designing A Composite Dictionary Adaptively From Joint Examples
We study the complementary behaviors of external and internal examples in image restoration, and are motivated to formulate a composite dictionary design framework.
Learning Super-Resolution Jointly from External and Internal Examples
Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a lowresolution (LR) input.
An Analysis of Unsupervised Pre-training in Light of Recent Advances
We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low.
Ranked #93 on
Image Classification
on STL-10
Decomposition-Based Domain Adaptation for Real-World Font Recognition
We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data.
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
Modeling the underlying data distribution by nonparametric kernel density estimation, the generalization error bounds for both unsupervised nonparametric classifiers are the sum of nonparametric pairwise similarity terms between the data points for the purpose of clustering.
Learning Locally-Adaptive Decision Functions for Person Verification
This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule.
Nonparametric Unsupervised Classification
Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data.
Learning to Search Efficiently in High Dimensions
Traditional approaches relied on algorithmic constructions that are often data independent (such as Locality Sensitive Hashing) or weakly dependent (such as kd-trees, k-means trees).
Graph Regularized Nonnegative Matrix Factorization for Data Representation
In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization, which respects the graph structure.
Bregman Distance to L1 Regularized Logistic Regression
In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model.
