collaborators

6 papers

cs.CV2025

ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery

Ching-Heng Cheng, Chih-Chung Hsu

Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-…

cs.CV2025

WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement

Ching-Heng Cheng, Jen-Wei Lee, Chia-Ming Lee +1

Underwater Image Enhancement (UIE) aims to restore visibility and correct color distortions caused by wavelength-dependent absorption and scattering. Recent hybrid approaches, whic…

cs.CV2025

Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding

Chih-Chung Hsu, I-Hsuan Wu, Wen-Hai Tseng +4

This report presents our semantic segmentation framework developed by team ACVLAB for the ICRA 2025 GOOSE 2D Semantic Segmentation Challenge, which focuses on parsing outdoor scene…

cs.CV2025

3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results

Benjamin Kiefer, Lojze Žust, Jon Muhovič +43

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive…

eess.IV2024

PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation

Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin +5

Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model. However, e…

cs.CV2024

Self-supervised Fusarium Head Blight Detection with Hyperspectral Image and Feature Mining

Yu-Fan Lin, Ching-Heng Cheng, Bo-Cheng Qiu +3

Fusarium Head Blight (FHB) is a serious fungal disease affecting wheat (including durum), barley, oats, other small cereal grains, and corn. Effective monitoring and accurate detec…