Search Results for author: Cheng Xie

Found 6 papers, 0 papers with code

LegoNet: Alternating Model Blocks for Medical Image Segmentation

no code implementations6 Jun 2023 Ikboljon Sobirov, Cheng Xie, Muhammad Siddique, Parijat Patel, Kenneth Chan, Thomas Halborg, Christos Kotanidis, Zarqiash Fatima, Henry West, Keith Channon, Stefan Neubauer, Charalambos Antoniades, Mohammad Yaqub

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters.

Image Segmentation Medical Image Segmentation +2

EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction

no code implementations29 Sep 2021 Chao Shu, Zhuoran Xin, Cheng Xie

The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials.

Graph Attention Graph Representation Learning +1

Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning

no code implementations23 Feb 2021 Hongxin Xiang, Cheng Xie, Ting Zeng, Yun Yang

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL).

Generalized Zero-Shot Learning Retrieval

Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization

no code implementations25 Jan 2021 Cheng Xie, Hongxin Xiang, Ting Zeng, Yun Yang, Beibei Yu, Qing Liu

CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network.

Image Classification Retrieval +1

Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

no code implementations ICLR 2018 Glen Berseth, Cheng Xie, Paul Cernek, Michiel Van de Panne

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment.

Continuous Control reinforcement-learning +2

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