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20212026
most citedRethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection

Hang-Cheng Dong, Guodong Liu, Dong Ye +1

Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead…

cs.CV2026

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao +5

The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reli…

cs.CV2025

Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects

Hang-Cheng Dong, Yibo Jiao, Fupeng Wei +3

Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects spatially. I…

cs.CV2025

Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception

Hang-Cheng Dong, Lu Zou, Bingguo Liu +2

Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhanced the automation level of dete…

cs.CV2023★ 1 cited

Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise

Hang-Cheng Dong, Yuhao Jiang, Yingyan Huang +4

Class activation maps are widely used for explaining deep neural networks. Due to its ability to highlight regions of interest, it has evolved in recent years as a key step in weak…

cs.LG2021

Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation

Hangcheng Dong, Jingxiao Liao, Yan Wang +4

Recently, the interpretability of deep learning has attracted a lot of attention. A plethora of methods have attempted to explain neural networks by feature visualization, saliency…

cs.LG2021

How to Explain Neural Networks: an Approximation Perspective

Hangcheng Dong, Bingguo Liu, Fengdong Chen +2

The lack of interpretability has hindered the large-scale adoption of AI technologies. However, the fundamental idea of interpretability, as well as how to put it into practice, re…