Search Results for author: Chuan Sheng Foo

Found 7 papers, 3 papers with code

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

no code implementations10 Aug 2021 Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram, Chao Li, Chuan Sheng Foo, Pavitra Krishnaswamy

Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data.

Classification Image Classification +1

Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs

no code implementations CVPR 2021 Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo

Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.

Active Learning Semantic Segmentation

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach

no code implementations18 Jan 2021 Xian Shi, Xun Xu, Ke Chen, Lile Cai, Chuan Sheng Foo, Kui Jia

Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets.

Active Learning Point Cloud Segmentation +1

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition

1 code implementation13 Jan 2021 Govind Narasimman, Kangkang Lu, Arun Raja, Chuan Sheng Foo, Mohamed Sabry Aly, Jie Lin, Vijay Chandrasekhar

Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem.

Activity Recognition

TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks

no code implementations29 Nov 2018 Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar, Mohamed M. Sabry

To this end, we introduce TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures.

General Classification Image Classification +1

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