Search Results for author: Guo-Sen Xie

Found 15 papers, 6 papers with code

Region Graph Embedding Network for Zero-Shot Learning

no code implementations ECCV 2020 Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao

To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.

Graph Embedding Zero-Shot Learning

Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification

no code implementations ECCV 2020 Fang Zhao, Shengcai Liao, Guo-Sen Xie, Jian Zhao, Kaihao Zhang, Ling Shao

On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.

Person Re-Identification Unsupervised Domain Adaptation

Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation

1 code implementation20 Jun 2022 Tao Chen, Yazhou Yao, Lei Zhang, Qiong Wang, Guo-Sen Xie, Fumin Shen

Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.

Weakly-Supervised Semantic Segmentation

Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

no code implementations CVPR 2022 Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, Efstratios Gavves

Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels.

Few-Shot Semantic Segmentation Semantic Segmentation

MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning

1 code implementation CVPR 2022 Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, Xinge You

Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.

Transfer Learning Zero-Shot Learning

TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

1 code implementation16 Dec 2021 Shiming Chen, Ziming Hong, Guo-Sen Xie, Jian Zhao, Hao Li, Xinge You, Shuicheng Yan, Ling Shao

Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.

Zero-Shot Learning

TransZero: Attribute-guided Transformer for Zero-Shot Learning

1 code implementation3 Dec 2021 Shiming Chen, Ziming Hong, Yang Liu, Guo-Sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Ke Lu, Xinge You

Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.

Zero-Shot Learning

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

1 code implementation NeurIPS 2021 Shiming Chen, Guo-Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao

Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.

Transfer Learning Zero-Shot Learning

Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation

no code implementations CVPR 2021 Guo-Sen Xie, Jie Liu, Huan Xiong, Ling Shao

However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects.

Few-Shot Semantic Segmentation Semantic Segmentation

Few-Shot Semantic Segmentation With Cyclic Memory Network

no code implementations ICCV 2021 Guo-Sen Xie, Huan Xiong, Jie Liu, Yazhou Yao, Ling Shao

Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask.

Few-Shot Semantic Segmentation Semantic Segmentation

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

1 code implementation11 Nov 2019 Shiming Chen, Peng Zhang, Guo-Sen Xie, Qinmu Peng, Zehong Cao, Wei Yuan, Xinge You

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.

Texture Synthesis

Attentive Region Embedding Network for Zero-Shot Learning

no code implementations CVPR 2019 Guo-Sen Xie, Li Liu, Xiaobo Jin, Fan Zhu, Zheng Zhang, Jie Qin, Yazhou Yao, Ling Shao

Zero-shot learning (ZSL) aims to classify images from unseen categories, by merely utilizing seen class images as the training data.

Zero-Shot Learning

SADIH: Semantic-Aware DIscrete Hashing

no code implementations3 Apr 2019 Zheng Zhang, Guo-Sen Xie, Yang Li, Sheng Li, Zi Huang

Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.

Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation

no code implementations29 Jan 2016 Guo-Sen Xie, Xu-Yao Zhang, Shuicheng Yan, Cheng-Lin Liu

Learned from a large-scale training dataset, CNN features are much more discriminative and accurate than the hand-crafted features.

Domain Adaptation Scene Recognition

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