Kehan Li

Orcid: 0000-0001-5739-909X

Affiliations:
  • Peking University, Beijing, China


According to our database1, Kehan Li authored at least 15 papers between 2022 and 2024.

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

Timeline

Legend:

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Bibliography

2024
Instance Brownian Bridge as Texts for Open-vocabulary Video Instance Segmentation.
CoRR, 2024

Parallel Vertex Diffusion for Unified Visual Grounding.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
FreestyleRet: Retrieving Images from Style-Diversified Queries.
CoRR, 2023

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation.
CoRR, 2023

Parallel Vertex Diffusion for Unified Visual Grounding.
CoRR, 2023

WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

DiffusionRet: Generative Text-Video Retrieval with Diffusion Model.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Position Embedding Needs an Independent Layer Normalization.
CoRR, 2022

Dynamic Clustering Network for Unsupervised Semantic Segmentation.
CoRR, 2022

Difference in Euclidean Norm Can Cause Semantic Divergence in Batch Normalization.
CoRR, 2022

Locality Guidance for Improving Vision Transformers on Tiny Datasets.
Proceedings of the Computer Vision, 2022


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