82 Result(s)
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Article
ULAF-Net: Ultra lightweight attention fusion network for real-time semantic segmentation
Real-time semantic segmentation, laying the foundation of mobile robots and autonomous driving, has attracted much attention in recent years. Currently, most deep models suffer high computational costs due to ...
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Article
Exploring and exploiting hierarchical structures for large-scale classification
Classification and recognition tasks confronted by intelligent systems are becoming complicated as the sizes of samples, dimensionality and labels dramatically increase in the past few years. Learning machines...
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Article
Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering
Incomplete multi-modal clustering (IMmC) is challenging due to the unexpected missing of some modalities in data. A key to this problem is to explore complementarity information among different samples with in...
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Article
Unsupervised deep hashing with multiple similarity preservation for cross-modal image-text retrieval
Deep hashing cross-modal image-text retrieval has the advantage of low storage cost and high retrieval efficiency by map** different modal data into a Hamming space. However, the existing unsupervised deep h...
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Chapter and Conference Paper
Weakly-Supervised Grounding for VQA with Dual Visual-Linguistic Interaction
Visual question answer (VQA) grounding, aimed at locating the visual evidence associated with the answers while answering questions, has attracted increasing research interest. To locate the evidence, most exi...
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Article
AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis
Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Exis...
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Article
Building hierarchical class structures for extreme multi-class learning
Class hierarchical structures play a significant role in large and complex tasks of machine learning. Existing studies on the construction of such structures follow a two-stage strategy. The category similarit...
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Article
Learning Self-supervised Low-Rank Network for Single-Stage Weakly and Semi-supervised Semantic Segmentation
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention ...
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Article
Data reduction based on NN-kNN measure for NN classification and regression
Data reduction processes are designed not only to reduce the amount of data, but also to reduce noise interference. In this study, we focus on researching sample reduction algorithms for the classification and...
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Chapter and Conference Paper
Self-Supervised Vision Transformer Based Nearest Neighbor Classification for Multi-Source Open-Set Domain Adaptation
Domain adaptation alleviates the performance drop when models are deployed in a target domain. Models assuming a close-set world fail in realistic open-set scenarios where novel classes not present in the sour...
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Article
Self-paced hierarchical metric learning (SPHML)
Metric learning aims to learn a distance to measure the difference between two samples, and it plays an important role in pattern recognition tasks. Most of the existing metric learning methods rely on pairs o...
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Chapter and Conference Paper
A Multilevel Inference Mechanism for User Attributes over Social Networks
In a real social network, each user has attributes for self-description called user attributes which are semantically hierarchical. With these attributes, we can implement personalized services such as user cl...
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Article
Tensorized Multi-view Subspace Representation Learning
Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of m...
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Chapter and Conference Paper
Interference Emitter Localization Based on Hyperbolic Passive Location in Spectrum Monitoring
Interference signals can always be found during spectrum monitoring, which has a serious impact in the regular use of radio business [1]. Sometimes is difficult to shied it by suppress signal, so it is becoming i...
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Chapter and Conference Paper
An Index Method for the Shortest Path Query on Vertex Subset for the Large Graphs
Shortest path query is an important problem in graphs and has been well-studied. In this paper, we study a special kind of shortest path query on a vertex subset. Most of the existing works propose various ind...
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Chapter and Conference Paper
Self-paced Robust Deep Face Recognition with Label Noise
Deep face recognition has achieved rapid development but still suffers from occlusions, illumination and pose variations, especially for face identification. The success of deep learning models in face recogni...
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Chapter and Conference Paper
Semi-interactive Attention Network for Answer Understanding in Reverse-QA
Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities. Existing QA studies assume that questions are raise...
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Chapter and Conference Paper
Visual Analysis for Online Communities Exploration Based on Social Data
As the result of Social media data being unprecedentedly available, we are provided so many substantial opportunities to explore social circle from many perspectives. Many researches have deep understandings o...
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Chapter and Conference Paper
Multi-task Sparse Regression Metric Learning for Heterogeneous Classification
With the ubiquitous usage of digital devices, social networks and industrial sensors, heterogeneous data explosively increase. Metric learning can boost the classification performance via jointly learning a se...
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Article
Multi-kernel SVM based depression recognition using social media data
Depression has become the world’s fourth major disease. Compared with the high incidence, however, the rate of depression medical treatment is very low because of the difficulty of diagnosis of mental problems...
