206 citations · 429 across the 7 of their papers we have counts for
7 papers
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Qi Bi, Shaodi You, Theo Gevers
Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike domain gap challenges, USSS is…
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Wei Ji, Jingjing Li, Qi Bi +3
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the…
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
Wei Ji, Jingjing Li, Qi Bi +3
Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep…
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
Qi Bi, Beichen Zhou, Kun Qin +2
Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly; 2) many objects irrelevant to the scene scheme are of…
Label-efficient Hybrid-supervised Learning for Medical Image Segmentation
Junwen Pan, Qi Bi, Yanzhan Yang +2
Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses…
Building change detection based on multi-scale filtering and grid partition
Qi Bi, Kun Qin, Han Zhang +3
Building change detection is of great significance in high resolution remote sensing applications. Multi-index learning, one of the state-of-the-art building change detection metho…
Multiple instance dense connected convolution neural network for aerial image scene classification
Qi Bi, Kun Qin, Zhili Li +2
With the development of deep learning, many state-of-the-art natural image scene classification methods have demonstrated impressive performance. While the current convolution neur…