Peng Lei
Oregon State University
15 Papers
25 Citations
Peng Lei is an academic researcher from Oregon State University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 6, co-authored 12 publications. Previous affiliations of Peng Lei include Amazon.com & Fudan University.
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Papers
Temporal Deformable Residual Networks for Action Segmentation in Videos
Peng Lei,Sinisa Todorovic +1 more
- 18 Jun 2018
TL;DR: A new model - temporal deformable residual network (TDRN) - aimed at analyzing video intervals at multiple temporal scales for labeling video frames demonstrates that TDRN outperforms the state of the art in frame-wise segmentation accuracy, segmental edit score, and segmental overlap F1 score.
HiRF: Hierarchical Random Field for Collective Activity Recognition in Videos
Mohamed R. Amer,Peng Lei,Sinisa Todorovic +2 more
- 06 Sep 2014
TL;DR: This paper addresses the problem of recognizing and localizing coherent activities of a group of people, called collective activities, in video with a new deep model, called Hierarchical Random Field (HiRF), which models only hierarchical dependencies between model variables.
Weakly Supervised Energy-Based Learning for Action Segmentation
Jun Li,Peng Lei,Sinisa Todorovic +2 more
- 01 Oct 2019
TL;DR: A new constrained discriminative forward loss (CDFL) that is used for training the HMM and GRU under weak supervision and gives superior results to those of the state of the art on the benchmark Breakfast Action, Hollywood Extended, and 50Salads datasets.
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Qiuyu Chen,Wei Zhang,Ning Zhou,Peng Lei,Yi Xu,Yu Zheng,Jianping Fan +6 more
- 14 Jun 2020
TL;DR: In this paper, an adaptive fractional dilated convolution (AFDC) is proposed to incorporate the information of image aspect ratios to learn more robust models, where the interpolation of nearest two integer dilated kernels are used to cope with the misalignment of fractional sampling.
•Posted Content
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
TL;DR: An adaptive fractional dilated convolution (AFDC) is developed, which is aspect-ratio-embedded, composition-preserving and parameter-free, which can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner.
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