361 citations · 421 across the 16 of their papers we have counts for
16 papers
MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
Yu-Xiang Zeng, Jun-Wei Hsieh, Xin Li +1
Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We p…
FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Erkhembayar Ganbold +9
With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage f…
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search
Yu-Ming Zhang, Jun-Wei Hsieh, Chun-Chieh Lee +1
Various hand-designed CNN architectures have been developed, such as VGG, ResNet, DenseNet, etc., and achieve State-of-the-Art (SoTA) levels on different tasks. Neural Architecture…
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…
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Yu-Hsiang Wang, Jun-Wei Hsieh, Ping-Yang Chen +3
Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systemat…
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…
NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation
Yi-Chun Wang, Jun-Wei Hsieh, Ming-Ching Chang
Current NAS-based semantic segmentation methods focus on accuracy improvements rather than light-weight design. In this paper, we proposed a two-stage framework to design our NAS-b…
Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior Knowledge
Yu-Ming Zhang, Jun-Wei Hsieh, Chun-Chieh Lee +1
In the past decade, many architectures of convolution neural networks were designed by handcraft, such as Vgg16, ResNet, DenseNet, etc. They all achieve state-of-the-art level on d…
Class-Specific Channel Attention for Few-Shot Learning
Ying-Yu Chen, Jun-Wei Hsieh, Ming-Ching Chang
Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data. FSL is challenging because t…
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…
SFPN: Synthetic FPN for Object Detection
Yu-Ming Zhang, Jun-Wei Hsieh, Chun-Chieh Lee +1
FPN (Feature Pyramid Network) has become a basic component of most SoTA one stage object detectors. Many previous studies have repeatedly proved that FPN can caputre better multi-s…
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…
MS-DARTS: Mean-Shift Based Differentiable Architecture Search
Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen +3
Differentiable Architecture Search (DARTS) is an effective continuous relaxation-based network architecture search (NAS) method with low search cost. It has attracted significant a…
CSL-YOLO: A New Lightweight Object Detection System for Edge Computing
Yu-Ming Zhang, Chun-Chieh Lee, Jun-Wei Hsieh +1
The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a si…
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection
Ping-Yang Chen, Ming-Ching Chang, Jun-Wei Hsieh +1
This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in rec…
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh +3
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on co…