Search Results for author: Feng Hou

Found 9 papers, 5 papers with code

Improved Meta Learning for Low Resource Speech Recognition

no code implementations11 May 2022 Satwinder Singh, Ruili Wang, Feng Hou

We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach.

Meta-Learning Speech Recognition

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

no code implementations8 Apr 2022 Jin Yuan, Feng Hou, Yangzhou Du, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.

Domain Adaptation Self-Supervised Learning

A Survey of Visual Transformers

1 code implementation11 Nov 2021 Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).

Improving Entity Linking through Semantic Reinforced Entity Embeddings

1 code implementation ACL 2020 Feng Hou, Ruili Wang, Jun He, Yi Zhou

We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality.

Entity Embeddings Entity Linking +1

Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer

1 code implementation29 Dec 2020 Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He

Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.

14 Semantic Segmentation +1

Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes

no code implementations5 Oct 2019 Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong

Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease.

Tumor Segmentation

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