J
Jun Yu
Researcher at Hangzhou Dianzi University
Publications - 193
Citations - 10327
Jun Yu is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 38, co-authored 179 publications receiving 7667 citations. Previous affiliations of Jun Yu include Xiamen University & Jiangnan University.
Papers
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Proceedings ArticleDOI
Image-Based 3D Human Pose Recovery with Locality Sensitive Sparse Retrieval
Chaoqun Hong,Jun Yu,Xuhui Chen +2 more
TL;DR: This approach improves traditional methods by adopting locality sensitive sparse coding in the retrieving process, which incorporates a local similarity preserving term into the objective of sparse coding, which groups similar silhouettes to alleviate the instability of sparse codes.
Journal ArticleDOI
Long-Form Video Question Answering via Dynamic Hierarchical Reinforced Networks
TL;DR: A dynamic hierarchical reinforced network for open-ended long-form video question answering is introduced, which employs an encoder–decoder architecture with a dynamic hierarchical encoder and a reinforced decoder to generate natural language answers.
Proceedings ArticleDOI
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
TL;DR: Zhang et al. as discussed by the authors proposed a novel structural knowledge masking (SKM) strategy to use the scene graph structure as a priori to perform masked language modeling, which enhances the semantic alignments by eliminating the interference information within and across modalities.
Journal ArticleDOI
Multi-view hypergraph learning by patch alignment framework
TL;DR: A novel semi-supervised dimensionality reduction method for multi-view data that assumes the hyperedges in hypergraph as patches and applies hypergraph to the patch alignment framework and gets dimensionality-reduced data by solving the standard eigen-decomposition to obtain the projection matrix.
Book
Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research
Jun Yu,Dacheng Tao +1 more
TL;DR: This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; andMultiview distance metric learning.