Yi Zhang

Orcid: 0000-0003-0843-7170

Affiliations:
  • Chongqing University, Chongqing, China


According to our database1, Yi Zhang authored at least 13 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Learning the Difference of Few-Shot Food Data Using Multivariate Knowledge-Guided Variational Autoencoder.
IEEE J. Biomed. Health Informatics, July, 2025

Learning Feature Exploration and Selection With Handcrafted Features for Few-Shot Learning.
IEEE Trans. Syst. Man Cybern. Syst., April, 2025

Rethinking the sample relations for few-shot classification.
Image Vis. Comput., 2025

Learning complementary visual information for few-shot food recognition by Regional Erasure and Reactivation.
Expert Syst. Appl., 2025

2024
Semi-Identical Twins Variational AutoEncoder for Few-Shot Learning.
IEEE Trans. Neural Networks Learn. Syst., July, 2024

Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Anchor-based discriminative dual distribution calibration for transductive zero-shot learning.
Image Vis. Comput., September, 2023

ASDFL: An adaptive super-pixel discriminative feature-selective learning for vehicle matching.
Expert Syst. J. Knowl. Eng., 2023

Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Adversarial Bidirectional Feature Generation for Generalized Zero-Shot Learning Under Unreliable Semantics.
Proceedings of the Pattern Recognition and Computer Vision - 5th Chinese Conference, 2022

Dual Space Multiple Instance Representative Learning for Medical Image Classification.
Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning.
CoRR, 2021

Generally Boosting Few-Shot Learning with HandCrafted Features.
Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021


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