no code implementations • 23 Apr 2024 • Wenjin Hou, Shiming Chen, Shuhuang Chen, Ziming Hong, Yan Wang, Xuetao Feng, Salman Khan, Fahad Shahbaz Khan, Xinge You
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.
no code implementations • 11 Apr 2024 • Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan
ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information.
no code implementations • 7 Apr 2024 • Zhimeng Xin, Shiming Chen, Tianxu Wu, Yuanjie Shao, Weiping Ding, Xinge You
This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions.
no code implementations • 18 Jan 2024 • Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.
no code implementations • 15 Sep 2023 • Zhimeng Xin, Tianxu Wu, Shiming Chen, Yixiong Zou, Ling Shao, Xinge You
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.
no code implementations • 19 Aug 2023 • Shiming Chen, Shihuang Chen, Wenjin Hou, Weiping Ding, Xinge You
However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.
Generative Adversarial Network Neural Architecture Search +1
no code implementations • 12 Jun 2023 • Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang
After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.
no code implementations • 29 Jan 2023 • Guanglin Zhou, Shaoan Xie, GuangYuan Hao, Shiming Chen, Biwei Huang, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao, Kun Zhang
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
no code implementations • ICCV 2023 • Changfeng Yu, Shiming Chen, Yi Chang, Yibing Song, Luxin Yan
To solve this dilemma, we propose a physical alignment and controllable generation network (PCGNet) for diverse and realistic rain generation.
2 code implementations • 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.
1 code implementation • 16 Dec 2021 • Shiming Chen, Ziming Hong, Wenjin Hou, Guo-Sen Xie, Yibing Song, Jian Zhao, 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.
1 code implementation • 3 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.
2 code implementations • 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.
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.
no code implementations • 8 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.
1 code implementation • 21 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.
1 code implementation • 11 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.
no code implementations • 15 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.