J
Junjie Cai
Researcher at University of Texas at San Antonio
Publications - 20
Citations - 747
Junjie Cai is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Feature (computer vision) & Semantics. The author has an hindex of 10, co-authored 19 publications receiving 639 citations. Previous affiliations of Junjie Cai include University of Science and Technology of China & Canon Inc..
Papers
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Book ChapterDOI
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
TL;DR: An approach for melanoma recognition in dermoscopy images that combines deep learning, sparse coding, and support vector machine SVM learning algorithms is presented, suggesting the proposed approach is an effective improvement over prior state-of-art.
Journal ArticleDOI
Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud
TL;DR: This paper constructs a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which narrows the semantic gap by combining both attributes and indexing into a single framework and designs an online cloud service to provide a more efficient online multimedia service.
Proceedings ArticleDOI
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perceptions
TL;DR: In this article, a multi-task learning framework was proposed to predict steering angle and speed control simultaneously in an end-to-end manner, where the steering angle alone is not sufficient for vehicle control.
Posted Content
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
TL;DR: This work proposes a multi-modal multi-task network to predict speed values and steering angles by taking previous feedback speeds and visual recordings as inputs and improves the failure data synthesis methods to solve the problem of error accumulation in real road tests.
Journal ArticleDOI
An Attribute-Assisted Reranking Model for Web Image Search
TL;DR: This paper proposes a visual-attribute joint hypergraph learning approach to simultaneously explore two information sources to exploit semantic attributes for image search reranking.