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- A survey of appearance models in visual object tracking771
This survey provides a detailed review of the existing 2D appearance models for visual object tracking and takes a module-based architecture that enables readers to easily grasp the key points ofVisual object tracking.
- Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods666
This survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.
- Spectral clustering for multi-type relational data288
A general model, the collective factorization on related matrices, is proposed for multi-type relational data clustering and a novel algorithm is derived, the spectral relational clustering, to cluster multi- type interrelated data objects simultaneously.
- Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality274
The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality.
- Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition263
A novel spatio-temporal graph routing (STGR) scheme for skeletonbased action recognition, which adaptively learns the intrinsic high-order connectivity relationships for physicallyapart skeleton joints, is proposed.
- A General Model for Multiple View Unsupervised Learning244
The proposed model introduces the concept of mapping function to make the different patterns from different pattern spaces comparable and hence an optimal pattern can be learned from the multiple patterns of multiple representations.
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- Co-clustering by block value decomposition197
A new co-clustering framework, block value decomposition(BVD), is presented, which factorizes the dyadic data matrix into three components, the row- coefficient matrix R, the block value matrix B, and the column-coefficient matrix C, which iteratively computes the three decomposition matrices based on the multiplicative updating rules.
- Episode-Based Prototype Generating Network for Zero-Shot Learning178
A novel generative model is proposed that synthesizes visual prototypes conditioned on the class semantic prototypes into an adversarial framework paired with a parameter-economic Multi-modal Cross-Entropy Loss to capture the discriminative information.
- Unsupervised learning on k-partite graphs160
A general model, the relation summary network, is proposed to find the hidden structures (the local cluster structures and the global community structures) from a k-partite graph to provide a principal framework for unsupervised learning on k- partite graphs of various structures.
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- Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking152
Experimental evaluations against state-of-the-art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm, and its applications to foreground segmentation and object tracking.
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- Intelligent Indexing and Semantic Retrieval of Multimodal Documents149
This research addresses the problem of finding pictures by optimally combining text and image similarity in a MMIR system and presents a general model for multimodal information retrieval that addresses the following issues: users' information need, and determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need.
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- Improved prototypical networks for few-Shot learning132
This work proposes an attention-analogous strategy to explore the class distribution information by distributing different weights to samples based on their representativeness and proposes a distance scaling strategy to reduce the intra- class difference while enlarge the inter-class difference.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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