142 citations · 159 across the 7 of their papers we have counts for
7 papers
Experimental Measurement-Device-Independent Quantum Key distribution with Flawed State-Preparation over 300 km
Yi-Fei Lu, Yan-Yang Zhou, Yang Wang +10
Quantum key distribution (QKD) promises theoretically secure communication. However, it encounters challenges in implementation security and performance due to inevitable device im…
A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation
Kai Huang, Mingfei Cheng, Yang Wang +4
Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent…
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Zhongwei Wan, Xin Liu, Benyou Wang +5
Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try…
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
Jason Dai, Ding Ding, Dongjie Shi +13
Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for bot…
Deep Discriminative Representation Learning with Attention Map for Scene Classification
Jun Li, Daoyu Lin, Yang Wang +2
Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were b…
BigDL: A Distributed Deep Learning Framework for Big Data
Jason Dai, Yiheng Wang, Xin Qiu +17
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning application…
Learning Semantic Segmentation with Diverse Supervision
Linwei Ye, Zhi Liu, Yang Wang
Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation. However, learning these models requires a large amoun…