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Found 14 papers, 7 papers with code
An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation
First, a high-precision rotation estimation method based on normal vector coplanarity constraints that consider the uncertainty of observations is proposed, which can be solved by Levenberg-Marquardt (LM) algorithm efficiently.
VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis
Generalist foundation model has ushered in newfound capabilities in medical domain.
Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data.
CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading
Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics.
Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration
To tackle this challenge, we proposed a pioneering two-stage hybrid Convolutional Neural Network (CNN) architecture, which connects segmentation and pose estimation in tandem.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology.
CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation
Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature.
Rethinking Blur Synthesis for Deep Real-World Image Deblurring
In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network architecture design.
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic.
S2Looking: A Satellite Side-Looking Dataset for Building Change Detection
This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles.
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
We evaluate CorDEL with extensive experiments conducted on both public benchmark datasets and a real-world dataset.
Ranked #7 on
Entity Resolution
on Amazon-Google
Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
