M
Ming-Ming Cheng
Researcher at Nankai University
Publications - 188
Citations - 31513
Ming-Ming Cheng is an academic researcher from Nankai University. The author has contributed to research in topics: Convolutional neural network & Image segmentation. The author has an hindex of 59, co-authored 188 publications receiving 21126 citations. Previous affiliations of Ming-Ming Cheng include University of Amsterdam & Beijing Institute of Technology.
Papers
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Proceedings ArticleDOI
Global contrast based salient region detection
TL;DR: This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
Journal ArticleDOI
Res2Net: A New Multi-Scale Backbone Architecture
TL;DR: Res2Net as mentioned in this paper constructs hierarchical residual-like connections within one single residual block to represent multi-scale features at a granular level and increases the range of receptive fields for each network layer.
Journal ArticleDOI
Struck: Structured Output Tracking with Kernels
Sam Hare,Stuart Golodetz,Amir Saffari,Vibhav Vineet,Ming-Ming Cheng,Stephen Hicks,Philip H. S. Torr +6 more
TL;DR: A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance.
Journal ArticleDOI
Salient Object Detection: A Benchmark
TL;DR: It is found that the models designed specifically for salient object detection generally work better than models in closely related areas, which provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems.
Journal ArticleDOI
Deeply Supervised Salient Object Detection with Short Connections
TL;DR: A new saliency method is proposed by introducing short connections to the skip-layer structures within the HED architecture, which produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency, effectiveness, and simplicity over the existing algorithms.