3 papers
cs.AI2025
AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Eunsu Baek, Keondo Park, Jeonggil Ko +3
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs…
cs.CV2025
Frequency Composition for Compressed and Domain-Adaptive Neural Networks
Yoojin Kwon, Hongjun Suh, Wooseok Lee +3
Modern on-device neural network applications must operate under resource constraints while adapting to unpredictable domain shifts. However, this combined challenge-model compressi…
cs.CV2025
A Revisit to the Decoder for Camouflaged Object Detection
Seung Woo Ko, Joopyo Hong, Suyoung Kim +5
Camouflaged object detection (COD) aims to generate a fine-grained segmentation map of camouflaged objects hidden in their background. Due to the hidden nature of camouflaged objec…