Search Results for author:
Found 716 papers, 326 papers with code
Manifold Regularization for SIR with Rate Root-n Convergence
In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR).
MahNMF: Manhattan Non-negative Matrix Factorization
This paper presents Manhattan NMF (MahNMF) which minimizes the Manhattan distance between $X$ and $W^T H$ for modeling the heavy tailed Laplacian noise.
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis
The obtained lower bound of the generalization discrimination power overcomes both limitations of the classical result, i. e., it is applicable when $D$ and $N$ are proportionally large and provides a quantitative description of the generalization ability of FLDA in terms of the ratio $\gamma=D/N$ and the population discrimination power.
A Survey on Multi-view Learning
Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace.
Sparse Norm Filtering
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.
Semi-supervised Node Splitting for Random Forest Construction
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.
Multiview Hessian Discriminative Sparse Coding for Image Annotation
In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation.
Learning to Rank for Blind Image Quality Assessment
The proposed BIQA method is one of learning to rank.
Unmixing Incoherent Structures of Big Data by Randomized or Greedy Decomposition
Learning big data by matrix decomposition always suffers from expensive computation, mixing of complicated structures and noise.
Distortion-driven Turbulence Effect Removal using Variational Model
To solve, or at least reduce these effects, we propose a new scheme to recover a latent image from observed frames by integrating a new variational model and distortion-driven spatial-temporal kernel regression.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images.
Semi-Supervised Coupled Dictionary Learning for Person Re-identification
In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
Weakly Supervised Multiclass Video Segmentation
In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
Video Face Editing Using Temporal-Spatial-Smooth Warping
Editing faces in videos is a popular yet challenging aspect of computer vision and graphics, which encompasses several applications including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques.
Facial Feature Point Detection: A Comprehensive Survey
CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point.
Local Rademacher Complexity for Multi-label Learning
We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning.
Classification with Noisy Labels by Importance Reweighting
In this scenario, there is an unobservable sample with noise-free labels.
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.
A Comprehensive Survey on Pose-Invariant Face Recognition
The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems.
DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition
Inspired by the theory of Leitners learning box from the field of psychology, we propose DropSample, a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR).
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking.
A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition
In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition.
FaLRR: A Fast Low Rank Representation Solver
In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix).
Saliency Propagation From Simple to Difficult
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
Robust Face Recognition via Multimodal Deep Face Representation
The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE).
A New Low-Rank Tensor Model for Video Completion
On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain, which extends the traditional matrix nuclear norm in a block circulant way.
An Experimental Survey on Correlation Filter-based Tracking
We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods.
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization
We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework.
Part-Stacked CNN for Fine-Grained Visual Categorization
In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy.
Ranked #62 on
Fine-Grained Image Classification
on CUB-200-2011
Dimensionality-Dependent Generalization Bounds for $k$-Dimensional Coding Schemes
Can we obtain dimensionality-dependent generalization bounds for $k$-dimensional coding schemes that are tighter than dimensionality-independent bounds when data is in a finite-dimensional feature space?
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #7 on
Image Dehazing
on RS-Haze
Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification
In the second step, hypergraph is used to represent the high order relationships between each datum and its prominent samples by regarding them as a hyperedge.
Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing.
Streaming Label Learning for Modeling Labels on the Fly
The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers.
Streaming View Learning
An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed.
Multilinear Hyperplane Hashing
Despite its successful progress in classic point-to-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up in many applications like active learning with SVMs.
Conditional Graphical Lasso for Multi-Label Image Classification
Multi-label image classification aims to predict multiple labels for a single image which contains diverse content.
Occlusion Boundary Detection via Deep Exploration of Context
Occlusion boundaries contain rich perceptual information about the underlying scene structure.
Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex Composite optimization
To the best of our knowledge, this is the first analysis of convergence rate of variance-reduced proximal stochastic gradient for non-convex composite optimization.
Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition
Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components.
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images.
Real Time Fine-Grained Categorization with Accuracy and Interpretability
The proposed architecture consists of a part localization network, a two-stream classification network that simultaneously encodes object-level and part-level cues, and a feature vectors fusion component.
On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization
Different from traditional unfolding based tensor norm, this low-rank tensor constraint has optimality properties similar to that of matrix rank derived from SVD, so the complementary information among views can be explored more efficiently and thoroughly.
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
Deep convolutional neural networks (CNNs) are successfully used in a number of applications.
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood.
Privileged Multi-label Learning
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.
Sparse Representation based Multi-sensor Image Fusion: A Review
As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion.
Tag Disentangled Generative Adversarial Networks for Object ImageRe-rendering
The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are complete/partially tagged(i. e., supervised/semi-supervised setting).
Algorithmic stability and hypothesis complexity
The bounds are based on martingale inequalities in the Banach space to which the hypotheses belong.
Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition
However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA.
Perceptual Adversarial Networks for Image-to-Image Transformation
The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks.
On Compressing Deep Models by Low Rank and Sparse Decomposition
Deep compression refers to removing the redundancy of parameters and feature maps for deep learning models.
Towards Evolutional Compression
In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters.
Transfer Learning with Label Noise
However, when learning this invariant knowledge, existing methods assume that the labels in source domain are uncontaminated, while in reality, we often have access to source data with noisy labels.
Beyond Filters: Compact Feature Map for Portable Deep Model
The filter is then re-configured to establish the mapping from original input to the new compact feature map, and the resulting network can preserve intrinsic information of the original network with significantly fewer parameters, which not only decreases the online memory for launching CNN but also accelerates the computation speed.
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering
For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.
A Compromise Principle in Deep Monocular Depth Estimation
However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions.
Learning with Bounded Instance- and Label-dependent Label Noise
Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN.
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
We propose a novel image classification method based on learning hierarchical inter-class structures.
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning
Specifically, we propose an end-to-end cooling control algorithm (CCA) that is based on the actor-critic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm.
A Joint Intrinsic-Extrinsic Prior Model for Retinex
We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image.
A Coarse-Fine Network for Keypoint Localization
To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers.
Centered Weight Normalization in Accelerating Training of Deep Neural Networks
Training deep neural networks is difficult for the pathological curvature problem.
Duality-free Methods for Stochastic Composition Optimization
We consider the composition optimization with two expected-value functions in the form of $\frac{1}{n}\sum\nolimits_{i = 1}^n F_i(\frac{1}{m}\sum\nolimits_{j = 1}^m G_j(x))+R(x)$, { which formulates many important problems in statistical learning and machine learning such as solving Bellman equations in reinforcement learning and nonlinear embedding}.
Variance Reduced methods for Non-convex Composition Optimization
In this paper, we apply the variance-reduced technique to derive two variance reduced algorithms that significantly improve the query complexity if the number of inner component functions is large.
Learning with Biased Complementary Labels
We therefore reason that the transition probabilities will be different.
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data.
Ranked #1 on
Face Sketch Synthesis
on CUFS
(FID metric)
Deep Sampling Networks
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
Saliency Preservation in Low-Resolution Grayscale Images
In this study, we explain the biological and computational motivation for LG, and show, through a range of human eye-tracking and computational modeling experiments, that saliency information is preserved in LG images.
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier
We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier.
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.
A survey on trajectory clustering analysis
This paper comprehensively surveys the development of trajectory clustering.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD).
Evolutionary Generative Adversarial Networks
In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance.
Attention-GAN for Object Transfiguration in Wild Images
This paper studies the object transfiguration problem in wild images.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations.
Semantic Edge Detection with Diverse Deep Supervision
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
Causal Generative Domain Adaptation Networks
For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains.
FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification
Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions.
Deep Motion Boundary Detection
In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet.
An Information-Theoretic View for Deep Learning
This upper bound shows that as the number of convolutional and pooling layers $L$ increases in the network, the expected generalization error will decrease exponentially to zero.
Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker
Object tracking is a hot topic in computer vision.
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification
Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring.
Semantic Structure-based Unsupervised Deep Hashing
Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency.
Anchor Cascade for Efficient Face Detection
Face detection is essential to facial analysis tasks such as facial reenactment and face recognition.
Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding
Visual grounding aims to localize an object in an image referred to by a textual query phrase.
Ranked #9 on
Phrase Grounding
on Flickr30k Entities Test
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition
Considering the complementarity between graph node convolution and graph edge convolution, we additionally construct two hybrid neural networks to combine graph node convolutional neural network and graph edge convolutional neural network using shared intermediate layers.
Dual Swap Disentangling
To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.
Bayesian Quantum Circuit
Parameterized quantum circuits (PQCs), as one of the most promising schemes to realize quantum machine learning algorithms on near-term quantum computers, have been designed to solve machine earning tasks with quantum advantages.
Quantum Physics
Stochastic Zeroth-order Optimization via Variance Reduction method
However, due to the variance in the search direction, the convergence rates and query complexities of existing methods suffer from a factor of $d$, where $d$ is the problem dimension.
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption
In this paper, we study the mixture proportion estimation (MPE) problem in a new setting: given samples from the mixture and the component distributions, we identify the proportions of the components in the mixture distribution.
Geometry-Aware Scene Text Detection With Instance Transformation Network
Localizing text in the wild is challenging in the situations of complicated geometric layout of the targets like random orientation and large aspect ratio.
Deep Ordinal Regression Network for Monocular Depth Estimation
These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions.
Ranked #13 on
Depth Estimation
on NYU-Depth V2
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
In the proposed networks, different levels of features at each spatial location are adaptively re-weighted according to the local structure and surrounding contextual information before aggregation.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Selective Zero-Shot Classification with Augmented Attributes
We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes.
Domain Generalization via Conditional Invariant Representation
With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X), Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains.
Robust Student Network Learning
In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network.
Instance-Dependent PU Learning by Bayesian Optimal Relabeling
Specifically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classifier with a consistency guarantee.
Correcting the Triplet Selection Bias for Triplet Loss
Considering that the number of triplets grows cubically with the size of training data, triplet mining is thus indispensable for efficiently training with triplet loss.
Deep Domain Generalization via Conditional Invariant Adversarial Networks
Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.
Ranked #67 on
Domain Generalization
on PACS
Context Refinement for Object Detection
Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions.
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem
In this paper, we consider the convex and non-convex composition problem with the structure $\frac{1}{n}\sum\nolimits_{i = 1}^n {{F_i}( {G( x )} )}$, where $G( x )=\frac{1}{n}\sum\nolimits_{j = 1}^n {{G_j}( x )} $ is the inner function, and $F_i(\cdot)$ is the outer function.
Towards Query Efficient Black-box Attacks: An Input-free Perspective
First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model.
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples.
A Grover-search Based Quantum Learning Scheme for Classification
Here we devise a Grover-search based quantum learning scheme (GBLS) to address the above two issues.
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
Trust region and cubic regularization methods have demonstrated good performance in small scale non-convex optimization, showing the ability to escape from saddle points.
Transfer Metric Learning: Algorithms, Applications and Outlooks
Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship.
The Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid quantum-classical machine learning scheme to accomplish generative tasks.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
An Optimal Transport View on Generalization
We derive upper bounds on the generalization error of learning algorithms based on their \emph{algorithmic transport cost}: the expected Wasserstein distance between the output hypothesis and the output hypothesis conditioned on an input example.
Generalization Bounds for Vicinal Risk Minimization Principle
First, we prove that the complexity of function classes convolving with vicinal functions can be controlled by that of the original function classes under the assumption that the function class is composed of Lipschitz-continuous functions.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries.
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.
Learning Versatile Filters for Efficient Convolutional Neural Networks
A series of secondary filters can be derived from a primary filter.
Learning Student Networks via Feature Embedding
Experiments on benchmark datasets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.
Multiple Sclerosis Lesion Inpainting Using Non-Local Partial Convolutions
However, existing methods inpaint lesions based on texture information derived from local surrounding tissue, often leading to inconsistent inpainting and the generation of artifacts such as intensity discrepancy and blurriness.
An Underwater Image Enhancement Benchmark Dataset and Beyond
In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images.
Ranked #5 on
Underwater Image Restoration
on LSUI
(using extra training data)
Fully-Featured Attribute Transfer
Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years.
Learning with Inadequate and Incorrect Supervision
To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges.
Image-Question-Answer Synergistic Network for Visual Dialog
Afterward, in the second stage, answers with high probability of being correct are re-ranked by synergizing with image and question.
Ranked #57 on
Visual Dialog
on Visual Dialog v1.0 test-std
MirrorGAN: Learning Text-to-image Generation by Redescription
Generating an image from a given text description has two goals: visual realism and semantic consistency.
Ranked #8 on
Text-to-Image Generation
on CUB
(Inception score metric)
Progressive LiDAR Adaptation for Road Detection
To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises.
Why ResNet Works? Residuals Generalize
This paper studies the influence of residual connections on the hypothesis complexity of the neural network in terms of the covering number of its hypothesis space.
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval
Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image.
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures.
Ranked #67 on
Monocular Depth Estimation
on KITTI Eigen split
On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning
The objective of our proposed method is to transform the features from different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized.
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer
We propose adversarial gated networks (Gated GAN) to transfer multiple styles in a single model.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge.
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention.
Cost-Sensitive Feature Selection by Optimizing F-Measures
Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features.
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification
The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel active learning strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross-dataset and intra-image; 3) the learned deep joint spectral-spatial feature representation is more generic and robust than many joint spectral-spatial feature representation.
Multi-View Intact Space Learning
In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data.
Diversified Hidden Markov Models for Sequential Labeling
While the first-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling, the basic model does not show satisfying performance when it is directly applied to real world problems, such as part-of-speech tagging (PoS tagging) and optical character recognition (OCR).
Optical Character Recognition
Optical Character Recognition (OCR)
+3
A Regularization Approach for Instance-Based Superset Label Learning
As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms.
Adapting Stochastic Block Models to Power-Law Degree Distributions
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
Fast Supervised Discrete Hashing
Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.
Multi-View Matrix Completion for Multi-Label Image Classification
Therefore, we propose to weightedly combine the MC outputs of different views, and present the multi-view matrix completion (MVMC) framework for transductive multi-label image classification.
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e. g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e. g. color, texture and shape).
Heterogeneous Multi-task Metric Learning across Multiple Domains
Heterogeneous transfer learning approaches can be adopted to remedy this drawback by deriving a metric from the learned transformation across different domains.
Decomposition-Based Transfer Distance Metric Learning for Image Classification
In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics.
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification
The features used in many image analysis-based applications are frequently of very high dimension.
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain
Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
Ensemble Teaching for Hybrid Label Propagation
More importantly, HyDEnT conducts propagation under the guidance of an ensemble of teachers.
Person Re-identification with Metric Learning using Privileged Information
We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space.
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features.
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification.
A Light Dual-Task Neural Network for Haze Removal
In LDTNet, the haze-free image and the transmission map are produced simultaneously.
Shakeout: A New Approach to Regularized Deep Neural Network Training
Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training.
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training.
Exploring Representativeness and Informativeness for Active Learning
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Meanwhile, it is also hard to build a good model without diagnosing discriminative labels.
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs).
Shared Predictive Cross-Modal Deep Quantization
In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search.
Patch alignment manifold matting
In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting.
Single Pixel Reconstruction for One-stage Instance Segmentation
In this paper, we propose a one-stage framework, SPRNet, which performs efficient instance segmentation by introducing a single pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors.
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification.
SCE: A manifold regularized set-covering method for data partitioning
Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function.
Coupled Learning for Facial Deblur
In this paper, we represent point spread functions (PSFs) by the linear combination of a set of pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp face image is represented by the linear combination of a set of pre-defined orthogonal face images.
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search
For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search.
Global Hashing System for Fast Image Search
Our methods are based on finding the tradeoff between the information losses in these two steps.
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data
Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measre of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image datasets.
Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
The exploration of quantum algorithms that possess quantum advantages is a central topic in quantum computation and quantum information processing.
Local Deep-Feature Alignment for Unsupervised Dimension Reduction
Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features.
Multiview Hessian Regularization for Image Annotation
One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it has been observed that LR biases the classification function towards a constant function which possibly results in poor generalization.
BIT: Biologically Inspired Tracker
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers
Such a perspective enables us to study deep multi-view learning in the context of regularized network training, for which we present control experiments of benchmark image classification to show the efficacy of our proposed CorrReg.
Robust subspace clustering by Cauchy loss function
This is due to that the CLF's influence function has a upper bound which can alleviate the influence of a single sample, especially the sample with a large noise, on estimating the residuals.
On Exploring Undetermined Relationships for Visual Relationship Detection
In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection.
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals.
A Distributed Approach towards Discriminative Distance Metric Learning
Distance metric learning is successful in discovering intrinsic relations in data.
Orthogonal Deep Neural Networks
To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices.
Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts
We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated.
Segmentation-Aware Image Denoising without Knowing True Segmentation
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step.
Truncated Cauchy Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers.
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
The model cascade (MC) strategy significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2015
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA).
Ranked #29 on
Video Question Answering
on ActivityNet-QA
Visual Question Answering (VQA)
Zero-Shot Video Question Answer
Soft-ranking Label Encoding for Robust Facial Age Estimation
Moreover, we also carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods.
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks.
CDPM: Convolutional Deformable Part Models for Semantically Aligned Person Re-identification
Part-level representations are essential for robust person re-identification.
Deep Modular Co-Attention Networks for Visual Question Answering
In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth.
Ranked #5 on
Question Answering
on SQA3D
The University of Sydney's Machine Translation System for WMT19
This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task.
Ranked #1 on
Machine Translation
on WMT 2018 Finnish-English
Slow Feature Analysis for Human Action Recognition
In this paper, we introduce the SFA framework to the problem of human action recognition by incorporating the discriminative information with SFA learning and considering the spatial relationship of body parts.
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Due to the stylish difference between synthetic and real images, we propose a temporally-consistent domain adaptation (TCDA) approach that simultaneously explores labels in the synthetic domain and temporal constraints in the videos to improve style transfer and depth prediction.
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems
In this paper, we propose a sublinear classical algorithm to tackle general minimum conical hull problems when the input has stored in a sample-based low-overhead data structure.
Bilinear Graph Networks for Visual Question Answering
The question-graph exchanges information between these output nodes from image-graph to amplify the implicit yet important relationship between objects.
Ranked #16 on
Visual Question Answering (VQA)
on VQA v2 test-std
Full-Stack Filters to Build Minimum Viable CNNs
Existing works used to decrease the number or size of requested convolution filters for a minimum viable CNN on edge devices.
Multimodal Unified Attention Networks for Vision-and-Language Interactions
Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents.
Recurrent Graph Syntax Encoder for Neural Machine Translation
Syntax-incorporated machine translation models have been proven successful in improving the model's reasoning and meaning preservation ability.
Investigation of wind pressures on tall building under interference effects using machine learning techniques
This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades.
