Search Results for author:
Found 7 papers, 4 papers with code
SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity
In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed based on both image and graph distances.
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift
Our result credits the best accuracy to the ResNet-101 model pre-trained on the Landmarks dataset for both verification and retrieval tasks by 84% and 24%, respectively.
Ranked #1 on
Image Classification
on AmsterTime
(using extra training data)
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
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 problem.
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training.
Attention-Aware Age-Agnostic Visual Place Recognition
Different from conventional VPR settings where the query images and gallery images come from the same domain, we propose a more common but challenging setup where the query images are collected under a new unseen condition.
Cross Domain Image Matching in Presence of Outliers
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 conditions in a training set, ii) lack of paired-image labels during training, iii) the existing of outliers that makes image matching domains not fully overlap.
Deep Visual City Recognition Visualization
(ii) A pretrained semantic segmentation model is used to label objects in pixel level, and then we introduce statistical measures to quantitatively evaluate the interpretability of discriminate objects.
