12 citations · 27 across the 7 of their papers we have counts for
7 papers
MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2
The Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment. This paper presents MicroViTv2, a lightweig…
FaceLiVTv2: An Improved Hybrid Architecture for Efficient Mobile Face Recognition
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2
Lightweight face recognition is increasingly important for deployment on edge and mobile devices, where strict constraints on latency, memory, and energy consumption must be met al…
Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety
Mas Nurul Achmadiah, Novendra Setyawan, Achmad Arif Bryantono +2
Recently, Image processing has advanced Faster and applied in many fields, including health, industry, and transportation. In the transportation sector, object detection is widely…
FaceLiVT: Face Recognition using Linear Vision Transformer with Structural Reparameterization For Mobile Device
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2
This paper introduces FaceLiVT, a lightweight yet powerful face recognition model that integrates a hybrid Convolution Neural Network (CNN)-Transformer architecture with an innovat…
Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving
Novendra Setyawan, Ghufron Wahyu Kurniawan, Chi-Chia Sun +2
The perception system is a a critical role of an autonomous driving system for ensuring safety. The driving scene perception system fundamentally represents an object detection tas…
MicroViT: A Vision Transformer with Low Complexity Self Attention for Edge Device
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2
The Vision Transformer (ViT) has demonstrated state-of-the-art performance in various computer vision tasks, but its high computational demands make it impractical for edge devices…
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding
Novendra Setyawan, Ghufron Wahyu Kurniawan, Chi-Chia Sun +3
Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in computer vision tasks. However, their deep architectures often lead to high computational…