72 citations · 73 across the 4 of their papers we have counts for
6 papers · 1 filter
BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
Sainan Liu, Shan Lin, Jingpei Lu +2
Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated…
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation
Shan Lin, Yuheng Zhi, Michael C. Yip
Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss. In this paper, we p…
Multi-Domain Adversarial Feature Generalization for Person Re-Identification
Shan Lin, Chang-Tsun Li, Alex C. Kot
With the assistance of sophisticated training methods applied to single labeled datasets, the performance of fully-supervised person re-identification (Person Re-ID) has been impro…
Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video
Shan Lin, Fangbo Qin, Haonan Peng +3
Deep learning-based methods have achieved promising results on surgical instrument segmentation. However, the high computation cost may limit the application of deep models to time…
Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
Fangbo Qin, Shan Lin, Yangming Li +3
Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument…
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification
Shan Lin, Haoliang Li, Chang-Tsun Li +1
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. Such…