Jin Li

Orcid: 0000-0002-9846-5951

According to our database1, Jin Li authored at least 12 papers between 2020 and 2025.

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

Timeline

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Bibliography

2025
Contrastive Learning via Variational Information Bottleneck.
IEEE Trans. Pattern Anal. Mach. Intell., September, 2025

2024
Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

UMG-CLIP: A Unified Multi-granularity Vision Generalist for Open-World Understanding.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Automatic Representative Frame Selection and Intrathoracic Lymph Node Diagnosis With Endobronchial Ultrasound Elastography Videos.
IEEE J. Biomed. Health Informatics, 2023

AiluRus: A Scalable ViT Framework for Dense Prediction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

VioLET: Vision-Language Efficient Tuning with Collaborative Multi-modal Gradients.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Progressively Compressed Auto-Encoder for Self-supervised Representation Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Adapting Shortcut with Normalizing Flow: An Efficient Tuning Framework for Visual Recognition.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Contrastive Regression for Domain Adaptation on Gaze Estimation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2020
FedMax: Enabling a Highly-Efficient Federated Learning Framework.
Proceedings of the 13th IEEE International Conference on Cloud Computing, 2020


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