5 citations · 8 across the 5 of their papers we have counts for
5 papers
MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes
Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen Chiu
Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through po…
SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
Chao-Peng Chen, Jun-Wei Hsieh, Ping-Yang Chen +2
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a be…
Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network
Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng +2
We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware loss function design for accurate crowd counting. Existing crowd-counting methods assume that the trainin…
Cooperative Reinforcement Learning on Traffic Signal Control
Chi-Chun Chao, Jun-Wei Hsieh, Bor-Shiun Wang
Traffic signal control is a challenging real-world problem aiming to minimize overall travel time by coordinating vehicle movements at road intersections. Existing traffic signal c…
Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks
Bor-Shiun Wang, Jun-Wei Hsieh, Ming-Ching Chang +3
Pooling is a simple but essential layer in modern deep CNN architectures for feature aggregation and extraction. Typical CNN design focuses on the conv layers and activation functi…