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Found 49 papers, 7 papers with code
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.
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.
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.
A Hybrid Loss for Multiclass and Structured Prediction
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs).
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.
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.
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.
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient
Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR).
Learning Graph Structure for Multi-Label Image Classification via Clique Generation
Exploiting label dependency for multi-label image classification can significantly improve classification performance.
Image-based Recommendations on Styles and Substitutes
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance.
Joint Probabilistic Data Association Revisited
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
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.
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields
The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors.
Joint Probabilistic Matching Using m-Best Solutions
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.
Blind Image Deconvolution by Automatic Gradient Activation
We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not.
Pairwise Matching Through Max-Weight Bipartite Belief Propagation
Feature matching is a key problem in computer vision and pattern recognition.
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization
Trace-norm regularization plays an important role in many areas such as machine learning and computer vision.
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
As a result, the representation power of intermediate layers can be very weak and the model becomes very redundant with limited performance.
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.
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks
We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
Bayesian Conditional Generative Adverserial Networks
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish.
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.
Joint Learning of Set Cardinality and State Distribution
We present a novel approach for learning to predict sets using deep learning.
Self-Paced Kernel Estimation for Robust Blind Image Deblurring
Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the estimation process.
Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
To preserve more original information, we propose an attentive recurrent neural network with similarity matrix based convolutional neural network (AR-SMCNN) model, which is able to capture comprehensive hierarchical information utilizing the advantages of both RNN and CNN.
Learning Deep Gradient Descent Optimization for Image Deconvolution
Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.
Deblurring Natural Image Using Super-Gaussian Fields
Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e. g., image gradients), which are insufficient to capture the complicated image structures.
Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal
Reflections often obstruct the desired scene when taking photos through glass panels.
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Compared to existing methods, MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization.
Variational Bayesian Dropout with a Hierarchical Prior
Then, we incorporate such a prior into inferring the joint posterior over network weights and the variance in the hierarchical prior, with which both the network training and the dropout rate estimation can be cast into a joint optimization problem.
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables
Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing.
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Ranked #19 on
3D Semantic Scene Completion
on NYUv2
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network.
Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
The installed amount of renewable energy has expanded massively in recent years.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction
Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation.
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs.
Learn to Predict Sets Using Feed-Forward Neural Networks
In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
Towards Deep Clustering of Human Activities from Wearables
Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data.
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments.
