79 citations · 93 across the 8 of their papers we have counts for
7 papers · 1 filter
SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity
Casper van Engelenburg, Seyran Khademi, Jan van Gemert
We propose a simple yet effective metric that measures structural similarity between visual instances of architectural floor plans, without the need for learning. Qualitatively, ou…
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift
Burak Yildiz, Seyran Khademi, Ronald Maria Siebes +1
We introduce AmsterTime: a challenging dataset to benchmark visual place recognition (VPR) in presence of a severe domain shift. AmsterTime offers a collection of 2,500 well-curate…
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
Zhaiyu Chen, Hugo Ledoux, Seyran Khademi +1
While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open prob…
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
Xiangwei Shi, Seyran Khademi, Yunqiang Li +1
Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training. Such visual…
Attention-Aware Age-Agnostic Visual Place Recognition
Ziqi Wang, Jiahui Li, Seyran Khademi +1
A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated…
Cross Domain Image Matching in Presence of Outliers
Xin Liu, Seyran Khademi, Jan C. van Gemert
Cross domain image matching between image collections from different source and target domains is challenging in times of deep learning due to i) limited variation of image conditi…
Deep Visual City Recognition Visualization
Xiangwei Shi, Seyran Khademi, Jan van Gemert
Understanding how cities visually differ from each others is interesting for planners, residents, and historians. We investigate the interpretation of deep features learned by conv…