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Found 24 papers, 2 papers with code
Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention
In this paper, we propose a novel deep learning architecture to improving word-level lip-reading.
Weakly-supervised Salient Instance Detection
Inspired by this insight, we propose to use class and subitizing labels as weak supervision for the SID problem.
Correlating Subword Articulation with Lip Shapes for Embedding Aware Audio-Visual Speech Enhancement
We first extract visual embedding from lip frames using a pre-trained phone or articulation place recognizer for visual-only EASE (VEASE).
Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL).
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives.
Where Is My Mirror?
To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach.
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs).
Active Object Reconstruction Using a Guided View Planner
Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner.
Learning Uncertain Convolutional Features for Accurate Saliency Detection
In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection.
Ranked #5 on
Saliency Detection
on DUT-OMRON
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Under this expression, the projection base of the model is based on the tensor CandeComp/PARAFAC (CP) decomposition and the number of free parameters in the model only grows linearly with the number of modes rather than exponentially.
Learning to Detect Salient Objects With Image-Level Supervision
In the second stage, FIN is fine-tuned with its predicted saliency maps as ground truth.
Localized LRR on Grassmann Manifolds: An Extrinsic View
Subspace data representation has recently become a common practice in many computer vision tasks.
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.
Matrix Variate RBM Model with Gaussian Distributions
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution.
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets.
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering.
TenSR: Multi-Dimensional Tensor Sparse Representation
In this paper, we propose a new sparse model TenSR based on tensor for MD data representation along with the corresponding MD sparse coding and MD dictionary learning algorithms.
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
As a significant subspace clustering method, low rank representation (LRR) has attracted great attention in recent years.
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework.
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
In a sparse representation based recognition scheme, it is critical to learn a desired dictionary, aiming both good representational power and discriminative performance.
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper.
Kernelized Low Rank Representation on Grassmann Manifolds
One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to.
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Many computer vision algorithms employ subspace models to represent data.
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.
