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
Deep Multimodal Neural Architecture Search
TL;DR: The obtained MMnasNet significantly outperforms existing state-of-the-art approaches across three multimodal learning tasks (over five datasets), including visual question answering, image-text matching, and visual grounding.
Journal ArticleDOI
Integrating multi-level deep learning and concept ontology for large-scale visual recognition
TL;DR: The experimental results on three image sets have demonstrated that the multi-level deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.
Journal ArticleDOI
Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings
TL;DR: This paper proposes a novel binary embedding-based zero-shot learning (BZSL) method, which recognizes the visual instances from unseen classes through an intermediate discriminative Hamming space, which well alleviates the visual-semantic bias problem.
Journal ArticleDOI
Zero-Shot Learning via Robust Latent Representation and Manifold Regularization
TL;DR: A novel framework to jointly learn the latent subspace and cross-modal embedding to link visual features with their semantic representations is formulated, such that the learned data representation is more discriminative to predict the semantic vectors, hence improving the overall classification performance.
Journal ArticleDOI
Automated Detection of Road Manhole and Sewer Well Covers From Mobile LiDAR Point Clouds
TL;DR: The detection results obtained from the road surface point clouds acquired by a RIEGL VMX-450 system show that the manhole and sewer well covers can be detected automatically and accurately.