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Rongrong Ji
Researcher at Xiamen University
Publications - 562
Citations - 20955
Rongrong Ji is an academic researcher from Xiamen University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 58, co-authored 483 publications receiving 14061 citations. Previous affiliations of Rongrong Ji include Columbia University & Harbin Institute of Technology.
Papers
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Book ChapterDOI
RGBD Salient Object Detection: A Benchmark and Algorithms
TL;DR: A simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency and a specialized multi-stage RGBD model is proposed which takes account of both depth and appearance cues derived from low-level feature contrast, mid-level region grouping and high-level priors enhancement.
Proceedings ArticleDOI
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
TL;DR: Experimental results and comparison with state-of-the-art methods show that the proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks.
Proceedings ArticleDOI
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin,Rongrong Ji,Chenqian Yan,Baochang Zhang,Liujuan Cao,Qixiang Ye,Feiyue Huang,David Doermann +7 more
TL;DR: This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
Proceedings ArticleDOI
Siamese Box Adaptive Network for Visual Tracking
TL;DR: SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN, making SiamB Ban more flexible and general.
Journal ArticleDOI
A novel features ranking metric with application to scalable visual and bioinformatics data classification
TL;DR: A Max-Relevance-Max-Distance (MRMD) feature ranking method, which balances accuracy and stability of feature ranking and prediction task, and runs faster than other filtering and wrapping methods, such as mRMR and Information Gain.