6 papers
STAS: Spatio-Temporal Adaptive Computation Time for Spiking Transformers
Donghwa Kang, Doohyun Kim, Sang-Ki Ko +3
Spiking neural networks (SNNs) offer energy efficiency over artificial neural networks (ANNs) but suffer from high latency and computational overhead due to their multi-timestep op…
Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
Donghwa Kang, Doohyun Kim, Sang-Ki Ko +3
State-of-the-art (SOTA) gradient-based adversarial attacks on spiking neural networks (SNNs), which largely rely on extending FGSM and PGD frameworks, face a critical limitation: s…
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
Woojin Shin, Donghwa Kang, Byeongyun Park +3
Detection Transformers (DETR) are increasingly adopted in autonomous vehicle (AV) perception systems due to their superior accuracy over convolutional networks. However, concurrent…
Real Time Scheduling Framework for Multi Object Detection via Spiking Neural Networks
Donghwa Kang, Woojin Shin, Cheol-Ho Hong +4
Given the energy constraints in autonomous mobile agents (AMAs), such as unmanned vehicles, spiking neural networks (SNNs) are increasingly favored as a more efficient alternative…
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks
Woojin Shin, Donghwa Kang, Daejin Choi +3
Multi-object tracking (MOT) aims to construct moving trajectories for objects, and modern multi-object trackers mainly utilize the tracking-by-detection methodology. Initial approa…
AT-SNN: Adaptive Tokens for Vision Transformer on Spiking Neural Network
Donghwa Kang, Youngmoon Lee, Eun-Kyu Lee +3
In the training and inference of spiking neural networks (SNNs), direct training and lightweight computation methods have been orthogonally developed, aimed at reducing power consu…