Search Results for author:
Found 61 papers, 11 papers with code
Compressed Hashing
Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search.
Learning to Rank for Blind Image Quality Assessment
The proposed BIQA method is one of learning to rank.
Lazy Random Walks for Superpixel Segmentation
Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time.
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.
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.
Cascade Learning by Optimally Partitioning
iCascade searches the optimal number ri of weak classifiers of each stage i by directly minimizing the computation cost of the cascade.
Learning Sampling Distributions for Efficient Object Detection
Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection.
Moving Object Detection in Video Using Saliency Map and Subspace Learning
So there are undesirable false alarms and missed alarms in many algorithms of moving object detection.
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details.
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Finally, we propose to combine both non-neighboring and neighboring features for pedestrian detection.
Ranked #28 on
Pedestrian Detection
on Caltech
Multi-View Subspace Clustering
In this paper, we propose a novel multi-view subspace clustering method.
A survey of sparse representation: algorithms and applications
The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers.
Cascaded Subpatch Networks for Effective CNNs
The first one is a linear filter of spatial size $ h\times w $ and is aimed at extracting features from spatial domain.
Learning Multilayer Channel Features for Pedestrian Detection
For example, CNN classifies these proposals by the full-connected layer features while proposal scores and the features in the inner-layers of CNN are ignored.
Ranked #25 on
Pedestrian Detection
on Caltech
Convolution in Convolution for Network in Network
In this paper, we propose to replace dense shallow MLP with sparse shallow MLP.
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community.
Temporal Multimodal Learning in Audiovisual Speech Recognition
Recently, audiovisual speech recognition based the MRBM has attracted much attention, and the MRBM shows its effectiveness in learning the joint representation across audiovisual modalities.
Constrained Low-Rank Learning Using Least Squares-Based Regularization
To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.
Object Discovery via Cohesion Measurement
Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix.
Query-Aware Sparse Coding for Multi-Video Summarization
Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest.
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
In this paper, we propose a generative approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which models the uncertainty observed in the data using latent stochastic variables.
Deep Binary Reconstruction for Cross-modal Hashing
Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion.
Image2song: Song Retrieval via Bridging Image Content and Lyric Words
Image is usually taken for expressing some kinds of emotions or purposes, such as love, celebrating Christmas.
Video Summarization with Attention-Based Encoder-Decoder Networks
This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence.
Ranked #4 on
Video Summarization
on TvSum
(using extra training data)
Exploring Models and Data for Remote Sensing Image Caption Generation
Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption.
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression.
Ranked #6 on
Scene Text Detection
on ICDAR 2013
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches.
HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization
Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results.
Deep Multimodal Clustering for Unsupervised Audiovisual Learning
And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
Deep Smoke Segmentation
According to results of our deep segmentation method, we can easily and accurately perform smoke detection from videos.
Triply Supervised Decoder Networks for Joint Detection and Segmentation
Experimental results on the VOC2007 and VOC2012 datasets demonstrate that the proposed TripleNet is able to improve both the detection and segmentation accuracies without adding extra computational costs.
Ranked #18 on
Semantic Segmentation
on PASCAL VOC 2012 test
Dense Multimodal Fusion for Hierarchically Joint Representation
Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities.
Deep LDA Hashing
The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does.
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries.
Patch alignment manifold matting
In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting.
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.
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
In this paper, we propose a weakly supervised adversarial domain adaptation to improve the segmentation performance from synthetic data to real scenes, which consists of three deep neural networks.
Listen to the Image
different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.
Multi-modal gated recurrent units for image description
And in the testing step, when an image is imported to our multi-modal GRU model, a sentence which describes the image content is generated.
3G structure for image caption generation
In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation.
Learning Feature Sparse Principal Components
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
A General Framework for Edited Video and Raw Video Summarization
Besides, the property-weights are learned for edited videos and raw videos, respectively.
A CNN-RNN Architecture for Multi-Label Weather Recognition
To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i. e., assigning an image more than one labels according to the displayed weather conditions.
Hierarchical Recurrent Neural Network for Video Summarization
Compared to traditional RNNs, H-RNN is more suitable to video summarization, since it can exploit long temporal dependency among frames, meanwhile, the computation operations are significantly lessened.
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.
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents.
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification.
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.
Vision-to-Language Tasks Based on Attributes and Attention Mechanism
To make full use of these information, this paper attempt to exploit the text guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language.
Intrinsic Weight Learning Approach for Multi-view Clustering
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering.
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.
Meta Learning for Task-Driven Video Summarization
Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote generalization ability of the trained model.
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently.
Adaptive Graph Auto-Encoder for General Data Clustering
Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem.
Pixel-Level Self-Paced Learning for Super-Resolution
Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields.
Unsupervised Graph Embedding via Adaptive Graph Learning
Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete.
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.
