Generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where data samples a...
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We present a self-attention-based method termed as Vision Transformer (ViT) to efficiently classify the human facial expressions. Our work can be divided into two contributions. First, the facial expression image is d...
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In this paper, we focus on the few-shot domain adaptation problem. With limited training data in target domain, a new approach is emerging to acquire the transferable knowledge from the source domain. Previous methods...
In this paper, we focus on the few-shot domain adaptation problem. With limited training data in target domain, a new approach is emerging to acquire the transferable knowledge from the source domain. Previous methods aligned the embedding space between domains by reducing the pair-wise distance. However, these methods are reporting the misalignment and poor generalization. To solve this problem, we propose a variational feature disentanglement framework. The embedding features are explicitly disentangled into domaininvariant and domain-specific components. The distributions of domain-invariant variance are estimated and aligned by the variational inference. For further disentanglement, the domain-invariant and domain-specific components are separated by the orthogonal constraints of subspaces. The experiments on Digits dataset and VisDA-C dataset demonstrate that the proposed method can outperform the state-of-the-art methods.
Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descrip...
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Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs. A series of methods represented by t...
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This paper proposes a facial expression recognition system for smart learning on classroom. Firstly, YOLO is used to extract face images of multiple students from high-resolution video;secondly, face images are prepro...
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作者：
Guo, KuoLi, YifanChen, HaoShen, Hong-BinYang, YangShanghai Jiao Tong University
Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University
Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University
School of Computer Science Computational Biology Department PittsburghPA15213 United States
Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bio...
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Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining ...
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Cross-modal hashing has been widely applied to retrieve items across modalities due to its superiority in fast computation and low storage. However, some challenges are still needed to address: (1) most existing CMH m...
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Cross-modal hashing has been widely applied to retrieve items across modalities due to its superiority in fast computation and low storage. However, some challenges are still needed to address: (1) most existing CMH methods take graphs, which are always predefined separately in each modality, as input to model data distribution. These methods omit to consider the correlation of graph structure among multiple modalities. Besides, cross-modal retrieval results highly rely on the quality of predefined affinity graphs;(2) most existing CMH methods deal with the preservation of intra- and inter-modal affinity independently to learn the binary codes, which ignores considering the fusion affinity among multi-modalities data;(3) most existing CMH methods relax the discrete constraints to solve the optimization objective, which could significantly degrade the retrieval performance. To solve the above limitations, in this paper, we propose a novel Anchor Graph Structure Fusion Hashing (AGSFH). AGSFH constructs the anchor graph structure fusion matrix from different anchor graphs of multiple modalities with the Hadamard product, which can fully exploit the geometric property of underlying data structure across multiple modalities. Specifically, based on the anchor graph structure fusion matrix, AGSFH makes an attempt to directly learn an intrinsic anchor graph, where the structure of the intrinsic anchor graph is adaptively tuned so that the number of components of the intrinsic graph is exactly equal to the number of clusters. Based on this process, training instances can be clustered into semantic space. Besides, AGSFH preserves the anchor fusion affinity into the common binary Hamming space, capturing intrinsic similarity and structure across modalities by hash codes. Furthermore, a discrete optimization framework is designed to learn the unified binary codes across modalities. Extensive experimental results on three public social datasets demonstrate the superiority of AGS
Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achi...
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