22 citations · 57 across the 9 of their papers we have counts for
9 papers
Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking
Sangyun Chung, Youngjoon Yu, Se Yeon Kim +2
Large-scale Vision-Language Models (VLMs) have achieved notable progress in aligning visual inputs with text. However, their ability to deeply understand the unique physical proper…
Revisiting Misalignment in Multispectral Pedestrian Detection: A Language-Driven Approach for Cross-modal Alignment Fusion
Taeheon Kim, Sangyun Chung, Youngjoon Yu +1
Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities…
SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models
Youngjoon Yu, Sangyun Chung, Byung-Kwan Lee +1
Large-scale Vision-Language Models (LVLMs) have significantly advanced with text-aligned vision inputs. They have made remarkable progress in computer vision tasks by aligning text…
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection
Taeheon Kim, Sebin Shin, Youngjoon Yu +2
RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem r…
MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection
Taeheon Kim, Sangyun Chung, Damin Yeom +3
Multispectral pedestrian detection is attractive for around-the-clock applications due to the complementary information between RGB and thermal modalities. However, current models…
Advancing Adversarial Training by Injecting Booster Signal
Hong Joo Lee, Youngjoon Yu, Yong Man Ro
Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have be…
Meta Input: How to Leverage Off-the-Shelf Deep Neural Networks
Minsu Kim, Youngjoon Yu, Sungjune Park +1
These days, although deep neural networks (DNNs) have achieved a noticeable progress in a wide range of research area, it lacks the adaptability to be employed in the real-world ap…
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Youngjoon Yu, Hong Joo Lee, Hakmin Lee +1
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of pe…
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim +2
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive p…