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Found 7 papers, 4 papers with code
Efficient Domain Adaptation for Endoscopic Visual Odometry
In this work, an efficient neural style transfer framework for endoscopic visual odometry is proposed, which compresses the time from pre-operative planning to testing phase to less than five minutes.
Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images
In this study, we conduct a detail analysis of the properties of endoscopic images and improve the compatibility of images and neural networks, to unleash the power of current neural networks.
Real-time Workload Pattern Analysis for Large-scale Cloud Databases
This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis.
SEA: A Scalable Entity Alignment System
To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings.
Unsupervised Entity Alignment for Temporal Knowledge Graphs
State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA.
Ranked #1 on
Entity Alignment
on YAGO-WIKI50K
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
Ranked #2 on
Entity Alignment
on DBP1M DE-EN
