F
Fuxin Li
Researcher at Oregon State University
Publications - 86
Citations - 4577
Fuxin Li is an academic researcher from Oregon State University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 28, co-authored 84 publications receiving 3806 citations. Previous affiliations of Fuxin Li include Georgia Institute of Technology & University of Bonn.
Papers
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Proceedings ArticleDOI
Multiple Hypothesis Tracking Revisited
TL;DR: It is demonstrated that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets, and it is shown that appearance models can be learned efficiently via a regularized least squares framework.
Proceedings ArticleDOI
Video Segmentation by Tracking Many Figure-Ground Segments
TL;DR: An unsupervised video segmentation approach by simultaneously tracking multiple holistic figure-ground segments that outperforms state-of-the-art approaches in the dataset, showing its efficiency and robustness to challenges in different video sequences.
Proceedings ArticleDOI
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
TL;DR: After detecting adversarial examples, it is shown that many of them can be recovered by simply performing a small average filter on the image, which should lead to more insights about the classification mechanisms in deep convolutional neural networks.
Book ChapterDOI
Open Set Learning with Counterfactual Images.
TL;DR: This work introduces a dataset augmentation technique that is based on generative adversarial networks that generates examples that are close to training set examples yet do not belong to any training category, and outperforms existing open set recognition algorithms on a selection of image classification tasks.
Book ChapterDOI
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video
TL;DR: Improved 3D structure and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences is demonstrated.