7 citations · 15 across the 6 of their papers we have counts for
6 papers · 1 filter
Adaptive Attention Link-based Regularization for Vision Transformers
Heegon Jin, Jongwon Choi
Although transformer networks are recently employed in various vision tasks with outperforming performance, extensive training data and a lengthy training time are required to trai…
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
Yonghyun Jeong, Doyeon Kim, Youngmin Ro +1
Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise f…
MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
Yonghyun Jeong, Doyeon Kim, Jaehyeon Lee +3
In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent…
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
Yonghyun Jeong, Doyeon Kim, Seungjai Min +3
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of…
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Youngmin Ro, Jongwon Choi, Dae Ung Jo +3
In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a la…
Context-aware Deep Feature Compression for High-speed Visual Tracking
Jongwon Choi, Hyung Jin Chang, Tobias Fischer +5
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The m…