Search Results for author: Shiming Chen

Found 9 papers, 7 papers with code

MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning

1 code implementation7 Mar 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

FREE: Feature Refinement for Generalized Zero-Shot Learning

1 code implementation ICCV 2021 Shiming Chen, Wenjie Wang, Beihao Xia, Qinmu Peng, Xinge You, Feng Zheng, Ling Shao

FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples.

Generalized Zero-Shot Learning

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction

no code implementations8 Oct 2020 Beihao Xia, Conghao Wong, Heng Li, Shiming Chen, Qinmu Peng, Xinge You

Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors.

Trajectory Prediction

CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network

1 code implementation21 Aug 2020 Shiming Chen, Wenjie Wang, Beihao Xia, Xinge You, Zehong Cao, Weiping Ding

In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.

GAN image forensics Image Generation

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

1 code implementation11 Nov 2019 Shiming Chen, Peng Zhang, Qinmu Peng, Zehong Cao, 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

Semi-supervised Feature Learning For Improving Writer Identification

no code implementations15 Jul 2018 Shiming Chen, Yisong Wang, Chin-Teng Lin, Weiping Ding, Zehong Cao

In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.

Data Augmentation

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