Search Results for author: Chuanyi Zhang

Found 8 papers, 4 papers with code

Data-driven Meta-set Based Fine-Grained Visual Classification

1 code implementation6 Aug 2020 Chuanyi Zhang, Yazhou Yao, Xiangbo Shu, Zechao Li, Zhenmin Tang, Qi Wu

To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.

Classification Fine-Grained Image Classification +3

Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

1 code implementation23 Jan 2021 Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, Zhenmin Tang

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.

Fine-Grained Visual Recognition

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

no code implementations CVPR 2021 Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.

Contrastive Learning Memorization

On the Study of Sample Complexity for Polynomial Neural Networks

no code implementations18 Jul 2022 Chao Pan, Chuanyi Zhang

As a general type of machine learning approach, artificial neural networks have established state-of-art benchmarks in many pattern recognition and data analysis tasks.

Face Recognition Image Generation

MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing

no code implementations18 Feb 2024 Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).

knowledge editing

Group Benefits Instances Selection for Data Purification

no code implementations23 Mar 2024 Zhenhuang Cai, Chuanyi Zhang, Dan Huang, Yuanbo Chen, Xiuyun Guan, Yazhou Yao

Comprehensive experimental results on synthetic and real-world datasets demonstrate the superiority of GRIP over the existing state-of-the-art methods.

Cannot find the paper you are looking for? You can Submit a new open access paper.