4 papers
FD-CanKD: Frequency-Decoupled Cross-Attention Distillation as a Refinement Prior for Compact Object Detectors
YoungJae Cheong, Jhonghyun An
Compact object detectors are suitable for resource-constrained visual perception, but their limited representation capacity creates an accuracy gap relative to large models. Conven…
Contrast-Guided Cross-Modal Distillation for Thermal Object Detection
SiWoo Kim, JhongHyun An
Robust perception at night remains challenging for thermal-infrared detection: low contrast and weak high-frequency cues lead to duplicate, overlapping boxes, missed small objects,…
Terrain-Enhanced Resolution-aware Refinement Attention for Off-Road Segmentation
Seongkyu Choi, Jhonghyun An
Off-road semantic segmentation suffers from thick, inconsistent boundaries, sparse supervision for rare classes, and pervasive label noise. Designs that fuse only at low resolution…
Source-Only Cross-Weather LiDAR via Geometry-Aware Point Drop
YoungJae Cheong, Jhonghyun An
Adverse weather conditions, such as rain, snow, and fog, severely degrade LiDAR semantic segmentation by introducing refraction, scattering, and point dropouts that compromise geom…