23 citations · 69 across the 10 of their papers we have counts for
19 papers · 1 filter
Optimal Correction Cost for Object Detection Evaluation
Mayu Otani, Riku Togashi, Yuta Nakashima +3
Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…
Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels
Nobukatsu Kajiura, Hong Liu, Shin'ichi Satoh
This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target obj…
Image Inpainting Guided by Coherence Priors of Semantics and Textures
Liang Liao, Jing Xiao, Zheng Wang +2
Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories rema…
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Yuting Liu, Zheng Wang, Miaojing Shi +3
Unsupervised crowd counting is a challenging yet not largely explored task. In this paper, we explore it in a transfer learning setting where we learn to detect and count persons i…
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao +7
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstru…
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
Liang Liao, Jing Xiao, Zheng Wang +2
Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted…