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- Sim‐Heng OngSuggested from co-authorship
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Works22 from public data
- 82
- Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving37
This work investigates diversity-based active learning (AL) as a potential solution to alleviate the annotation burden, and proposes a novel acquisition function that enforces spatial and temporal diversity in the selected samples.
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- Automatic transfer function design for medical visualization using visibility distributions and projective color mapping21
A system for automatic transfer function design based on visibility distribution and projective color mapping and an automatic color assignment scheme based on projective mapping is proposed to assign colors that allow for the visual discrimination of different structures, while also reflecting the degree of similarity between them.
- TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks20
This work introduces TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures and highlights the Pareto-optimal operating points that emphasize the necessity to explicitly consider hardware characteristics in the search process.
- A two-level clustering approach for multidimensional transfer function specification in volume visualization19
This paper proposes a novel volume exploration scheme that provides top-down navigation to users exploring the volume and keeps track of each interesting structure discovered, which not only enables users to inspect individual structures closely, but also allows them to compose the final visualization by fusing the structures deemed important.
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- Exploring Spatial Diversity for Region-Based Active Learning15
This work proposes that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning.
- Exploring Active Learning for Semiconductor Defect Segmentation14
This work explores active learning (AL) as a potential solution to alleviate the annotation burden of semiconductor XRM scans by proposing to perform contrastive pretraining on the unlabelled data to obtain the initialization weights for each AL cycle, and a rareness-aware acquisition function that favors the selection of samples containing rare classes.
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- Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization13
A rule‐enhanced transfer function design method is presented that allows important structures of the volume to be more effectively separated and highlighted and a rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues.
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- Towards Safe and Efficient Analog Circuit Design: Active Learning for Feasibility Region Exploration2
- Box-Level Class-Balanced Sampling For Active Object Detection2
This work proposes a class-balanced sampling strategy to select more objects from minority classes for labelling so as to make the final training data, ground truth labels obtained by AL and pseudo labels, more class-balanced to train a better model.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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