6 papers · 1 filter
Measurement Plasticity: Sensor-Level Adaptation for Vision-Language Models
Boyeong Im, Wooseok Lee, Yoojin Kwon +1
We propose Multi-View Physical-prompt (MVP) for Test-Time Adaptation (TTA), a forward-only framework that moves TTA from tokens to photons by treating the camera exposure triangle…
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…
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
Yegyu Han, Taegyoon Yoon, Dayeon Woo +2
Recent advances on 6D object pose estimation have achieved high performance on representative benchmarks such as LM-O, YCB-V, and T-Less. However, these datasets were captured unde…
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…
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…
Adaptive Camera Sensor for Vision Models
Eunsu Baek, Sunghwan Han, Taesik Gong +1
Domain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large labeled datasets to address. Inspired by…