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Found 370 papers, 160 papers with code
Adversarial Learning with Local Coordinate Coding
Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity.
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet).
Salient Object Detection by Lossless Feature Reflection
Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Salient object detection (SOD), which aims to find the most important region of interest and segment the relevant object/item in that area, is an important yet challenging vision task.
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Visual Question Answering with Memory-Augmented Networks
In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
In our work, we focus on the weakly supervised semantic segmentation with image label annotations.
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.
Agile Amulet: Real-Time Salient Object Detection with Contextual Attention
This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile Amulet) for salient object detection.
Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression
The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method.
Real-time Semantic Image Segmentation via Spatial Sparsity
We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results.
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning
The Visual Dialogue task requires an agent to engage in a conversation about an image with a human.
Ranked #4 on
Visual Dialog
on VisDial v0.9 val
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags.
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
To this end we propose a unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs.
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition
Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7. 5% over a strong baseline with moderate-sized real data being available.
Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network.
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Then we train fully convolutional deep residual networks to predict the depth label of each pixel.
FVQA: Fact-based Visual Question Answering
We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.
Ranked #2 on
Visual Question Answering (VQA)
on F-VQA
Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation.
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
The prior for the non-low-rank structure is established based on a mixture of Gaussians which is shown to be flexible enough, and powerful enough, to inform the completion process for a variety of real tensor data.
Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks
We study the problem of estimating the relative depth order of point pairs in a monocular image.
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications.
Ranked #11 on
Single-object discovery
on COCO_20k
Visually Aligned Word Embeddings for Improving Zero-shot Learning
To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE).
Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks
In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks.
TasselNet: Counting maize tassels in the wild via local counts regression network
To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.
Care about you: towards large-scale human-centric visual relationship detection
In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotation (nearly 10K categories) than the previous released datasets.
Human-Object Interaction Detection
Relationship Detection
+1
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine learning applications.
Exploring Context with Deep Structured models for Semantic Segmentation
We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions.
Towards Context-aware Interaction Recognition
Recognizing how objects interact with each other is a crucial task in visual recognition.
Robust Guided Image Filtering
In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above.
Structured Learning of Tree Potentials in CRF for Image Segmentation
In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.
Multi-Label Image Classification with Regional Latent Semantic Dependencies
Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity.
Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation
Semantic image segmentation is a fundamental task in image understanding.
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks.
Compositional Model based Fisher Vector Coding for Image Classification
To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions.
Cross-convolutional-layer Pooling for Image Recognition
Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation.
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions
To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best.
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.
Sequential Person Recognition in Photo Albums with a Recurrent Network
In this work, we propose to model the relational information between people as a sequence prediction task.
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition.
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation
Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision including object detection and semantic segmentation.
Fast Training of Triplet-based Deep Binary Embedding Networks
To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary code which serve as the label of each training datum.
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them.
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
Instance retrieval requires one to search for images that contain a particular object within a large corpus.
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
In this paper, we propose a deep end-to-end neu- ral network to simultaneously learn high-level features and a corresponding similarity metric for person re-identification.
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach
In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification.
Pushing the Limits of Deep CNNs for Pedestrian Detection
In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional neural network (DCNN) model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms.
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification
Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters.
Efficient piecewise training of deep structured models for semantic segmentation
Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs).
Ranked #54 on
Semantic Segmentation
on PASCAL Context
Structured learning of metric ensembles with application to person re-identification
However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.
Bridging Category-level and Instance-level Semantic Image Segmentation
(iii) As the performance of semantic category segmentation has a significant impact on the instance-level segmentation, which is the second step of our approach, we train fully convolutional residual networks to achieve the best semantic category segmentation accuracy.
Ranked #53 on
Semantic Segmentation
on PASCAL Context
Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications
Two standard relaxation methods are widely used for solving general BQPs--spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages.
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Crowd counting is an important task in computer vision, which has many applications in video surveillance.
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
(i) First, we evaluate different variations of a fully convolutional residual network so as to find the best configuration, including the number of layers, the resolution of feature maps, and the size of field-of-view.
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach.
Less is more: zero-shot learning from online textual documents with noise suppression
Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes.
Structured Learning of Binary Codes with Column Generation
Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures.
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution
To address this problem, we propose a novel approach by inspecting the distribution of the detection scores at multiple image regions based on the detector trained from the "regular object" and "other objects".
Order-aware Convolutional Pooling for Video Based Action Recognition
Most video based action recognition approaches create the video-level representation by temporally pooling the features extracted at each frame.
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss
We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems.
Data Driven Robust Image Guided Depth Map Restoration
Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution.
Explicit Knowledge-based Reasoning for Visual Question Answering
We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base.
Fast detection of multiple objects in traffic scenes with a common detection framework
The proposed framework consists of a dense feature extractor and detectors of three important classes.
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems.
Deeply Learning the Messages in Message Passing Inference
The network output dimension for message estimation is the same as the number of classes, in contrast to the network output for general CNN potential functions in CRFs, which is exponential in the order of the potentials.
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame.
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning
Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features.
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds.
Unsupervised Feature Learning for Dense Correspondences across Scenes
We experimentally demonstrate that the learned features, together with our matching model, outperforms state-of-the-art methods such as the SIFT flow, coherency sensitive hashing and the recent deformable spatial pyramid matching methods both in terms of accuracy and computation efficiency.
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition.
Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition
One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs.
Mid-level Deep Pattern Mining
We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used.
Efficient SDP Inference for Fully-connected CRFs Based on Low-rank Decomposition
Conditional Random Fields (CRF) have been widely used in a variety of computer vision tasks.
CRF Learning with CNN Features for Image Segmentation
The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels.
Learning to rank in person re-identification with metric ensembles
We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated.
Supervised Hashing Using Graph Cuts and Boosted Decision Trees
The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods.
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF.
Hashing on Nonlinear Manifolds
In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Our method is also much faster and more scalable than standard interior-point SDP solvers based WLDA.
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC).
Face Identification with Second-Order Pooling
Spatial pyramid pooling of features encoded by an over-complete dictionary has been the key component of many state-of-the-art image classification systems.
Face Image Classification by Pooling Raw Features
We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab code), which is mainly inspired by spatial pyramid pooling in generic image classification.
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.
Optimizing Ranking Measures for Compact Binary Code Learning
Hashing has proven a valuable tool for large-scale information retrieval.
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets.
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data.
Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification
In this work, we propose to learn deep convolutional image features using unsupervised and supervised learning.
Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations
We show that using Marginal Polytope Diagrams allows the number of constraints to be reduced without loosening the LP relaxations.
Large-margin Learning of Compact Binary Image Encodings
The use of high-dimensional features has become a normal practice in many computer vision applications.
Asymmetric Pruning for Learning Cascade Detectors
In this work, we present a new approach to train an effective node classifier in a cascade detector.
StructBoost: Boosting Methods for Predicting Structured Output Variables
Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost.
A Generalized Probabilistic Framework for Compact Codebook Creation
In the third criterion, which shows the best merging performance, we propose a max-margin-based parameter estimation method and apply it with multinomial distribution.
From Kernel Machines to Ensemble Learning
This finding not only enables us to design new ensemble learning methods directly from kernel methods, but also makes it possible to take advantage of those highly-optimized fast linear SVM solvers for ensemble learning.
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed.
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization
Different from most existing multi-class boosting methods, which use the same set of weak learners for all the classes, we train class specified weak learners (i. e., each class has a different set of weak learners).
Contextual Hypergraph Modelling for Salient Object Detection
In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions.
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry.
Online Unsupervised Feature Learning for Visual Tracking
Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications.
Efficient pedestrian detection by directly optimize the partial area under the ROC curve
We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning.
Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement.
Generic Image Classification Approaches Excel on Face Recognition
The main finding of this work is that the standard image classification pipeline, which consists of dictionary learning, feature encoding, spatial pyramid pooling and linear classification, outperforms all state-of-the-art face recognition methods on the tested benchmark datasets (we have tested on AR, Extended Yale B, the challenging FERET, and LFW-a datasets).
Characterness: An Indicator of Text in the Wild
In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration.
A General Two-Step Approach to Learning-Based Hashing
This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods.
A scalable stage-wise approach to large-margin multi-class loss based boosting
In this work, we propose a scalable and simple stage-wise multi-class boosting method, which also directly maximizes the multi-class margin.
Fast Approximate L_infty Minimization: Speeding Up Robust Regression
Minimization of the $L_\infty$ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.
A Fast Semidefinite Approach to Solving Binary Quadratic Problems
Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods.
Deep attention-based classification network for robust depth prediction
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
Troy: Give Attention to Saliency and for Saliency
In addition, our work has text overlap with arXiv:1804. 06242, arXiv:1705. 00938 by other authors.
Training Compact Neural Networks with Binary Weights and Low Precision Activations
In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption.
Towards Effective Deep Embedding for Zero-Shot Learning
Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier.
Diagnostics in Semantic Segmentation
Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene understanding and rapidly becoming vital in lots of real-world applications, including driverless cars and medical imaging.
Correlation Propagation Networks for Scene Text Detection
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role in many vision applications.
Face Recognition using Optimal Representation Ensemble
It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition.
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources.
Visual Question Answering as Reading Comprehension
In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem.
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks
Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches.
Positive Semidefinite Metric Learning with Boosting
In this work, we propose a boosting-based technique, termed BoostMetric, for learning a Mahalanobis distance metric.
PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning
In this work, we consider the problem of learning a positive semidefinite matrix.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss
Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs
Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty.
Learning Compact Binary Codes for Visual Tracking
A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes.
Part-Based Visual Tracking with Online Latent Structural Learning
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking.
Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs
Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task.
Learning Graph Structure for Multi-Label Image Classification via Clique Generation
Exploiting label dependency for multi-label image classification can significantly improve classification performance.
What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution
The key observation motivating our approach is that "regular object" images, "unusual object" images and "other objects" images exhibit different region-level scores in terms of both the score values and the spatial distributions.
Multi-Attention Network for One Shot Learning
One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image.
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior
To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.
Towards Context-Aware Interaction Recognition for Visual Relationship Detection
The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent.
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
Ranked #46 on
Semantic Segmentation
on PASCAL Context
Training Quantized Neural Networks with a Full-precision Auxiliary Module
In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function.
Architecture Search of Dynamic Cells for Semantic Video Segmentation
In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames.
Towards End-to-End Text Spotting in Natural Scenes
Text spotting in natural scene images is of great importance for many image understanding tasks.
V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices
One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.
MobileFAN: Transferring Deep Hidden Representation for Face Alignment
To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
Furthermore, we propose a second progressive quantization scheme which gradually decreases the bit-width from high-precision to low-precision during training.
Auxiliary Learning for Deep Multi-task Learning
The most current methods can be categorized as either: (i) hard parameter sharing where a subset of the parameters is shared among tasks while other parameters are task-specific; or (ii) soft parameter sharing where all parameters are task-specific but they are jointly regularized.
Structured Binary Neural Networks for Image Recognition
Experiments on both classification, semantic segmentation and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature.
Unified Multifaceted Feature Learning for Person Re-Identification
The loss structures the augmented images resulted by the two types of image erasing in a two-level hierarchy and enforces multifaceted attention to different parts.
To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions
Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training.
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation
3D point cloud semantic and instance segmentation is crucial and fundamental for 3D scene understanding.
Ranked #28 on
3D Instance Segmentation
on ScanNet(v2)
Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory
Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training.
Joint Deep Learning of Facial Expression Synthesis and Recognition
Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection
Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video.
Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes
Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutional layers is designed to generate high-resolution feature maps preserving the detailed spatial information.
Scope Head for Accurate Localization in Object Detection
Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance.
A Robust Attentional Framework for License Plate Recognition in the Wild
On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly.
FCOS: A simple and strong anchor-free object detector
In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications.
Deep Learning for Anomaly Detection: A Review
This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods.
Soft Expert Reward Learning for Vision-and-Language Navigation
In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
Representative Graph Neural Network
In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.
FATNN: Fast and Accurate Ternary Neural Networks
Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts.
Instance-Aware Embedding for Point Cloud Instance Segmentation
However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects.
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions
To our knowledge, DR1Mask is the first panoptic segmentation framework that exploits a shared feature map for both instance and semantic segmentation by considering both efficacy and efficiency.
Graph Attention Tracking
We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.
Diverse Knowledge Distillation for End-to-End Person Search
Person search aims to localize and identify a specific person from a gallery of images.
Single-path Bit Sharing for Automatic Loss-aware Model Compression
By jointly training the binary gates in conjunction with network parameters, the compression configurations of each layer can be automatically determined.
Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline
To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios.
Object Detection Made Simpler by Eliminating Heuristic NMS
On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors.
Instance and Panoptic Segmentation Using Conditional Convolutions
In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance.
Generic Perceptual Loss for Modeling Structured Output Dependencies
We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.
TFPose: Direct Human Pose Estimation with Transformers
We propose a human pose estimation framework that solves the task in the regression-based fashion.
Ranked #26 on
Pose Estimation
on MPII Human Pose
(using extra training data)
An Adversarial Human Pose Estimation Network Injected with Graph Structure
Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods.
Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition
In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
Scene flow in 3D point clouds plays an important role in understanding dynamic environments.
SOLO: A Simple Framework for Instance Segmentation
Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation.
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data
First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data.
Ranked #3 on
Facial Attribute Classification
on LFWA
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification
In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i. e., an elevated view of an object from above.
Occluded Person Re-Identification With Single-Scale Global Representations
However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians.
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation
Deep image matting methods have achieved increasingly better results on benchmarks (e. g., Composition-1k/alphamatting. com).
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth
However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency.
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings
The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets, despite not using any images therefrom.
Ranked #1 on
Semantic Segmentation
on WildDash
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.
Ranked #4 on
Long-tail Learning
on iNaturalist 2018
Efficient Video Segmentation Models with Per-frame Inference
To this end, we perform inference at each frame.
Improving Monocular Visual Odometry Using Learned Depth
The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes.
PointInst3D: Segmenting 3D Instances by Points
The current state-of-the-art methods in 3D instance segmentation typically involve a clustering step, despite the tendency towards heuristics, greedy algorithms, and a lack of robustness to the changes in data statistics.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
In this work, we tackle this challenging issue with a novel range view projection mechanism, and for the first time demonstrate the benefits of fusing multi-frame point clouds for a range-view based detector.
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations
Point annotations are considerably more time-efficient than bounding box annotations.
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior
In this work, we focus on scene flow learning on point clouds in a self-supervised manner.
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, however, resulting in low attack transferability and high vulnerability to denoising defense models.
