5 papers
Less Supervision, Better Generalization: Weakly Supervised Fake Region Localization in Diffusion-Edited Images
Junhee Lee, Donghyeon Jeon, Taeoh Kim +2
Localizing AI-edited regions is essential for interpretable forensic analysis, but remains challenging due to subtle and spatially distributed artifacts that are misaligned with se…
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection
KunHo Heo, Seungjae Kim, Wongyu Lee +2
Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent prototype-based similarity learnin…
RefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detection
Junhee Lee, ChaeBeen Bang, MyoungChul Kim +1
Weakly-Supervised Video Anomaly Detection aims to identify anomalous events using only video-level labels, balancing annotation efficiency with practical applicability. However, ex…
Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentation
Taeyeong Kim, SeungJoon Lee, Jung Uk Kim +1
Domain generalization in semantic segmentation faces challenges from domain shifts, particularly under adverse conditions. While diffusion-based data generation methods show promis…
PlugTrack: Multi-Perceptive Motion Analysis for Adaptive Fusion in Multi-Object Tracking
Seungjae Kim, SeungJoon Lee, MyeongAh Cho
Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency bu…