Yicheng He

Orcid: 0009-0002-6246-8592

According to our database1, Yicheng He authored at least 12 papers between 2015 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Viewpoint guided multi-stream neural network for skeleton action recognition.
Multim. Tools Appl., January, 2024

2023
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Research on the development of a digital conservation system for cultural relics based on image recognition technology.
Proceedings of the 3rd International Conference on Bioinformatics and Intelligent Computing, 2023

2022
View knowledge transfer network for multi-view action recognition.
Image Vis. Comput., 2022

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Uncovering Visually Impaired Gamers' Preferences for Spatial Awareness Tools Within Video Games.
Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility, 2022

Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Towards a Generalized Acoustic Minimap for Visually Impaired Gamers.
Proceedings of the UIST '21: The Adjunct Publication of the 34th Annual ACM Symposium on User Interface Software and Technology, 2021

Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Drug Selection via Joint Push and Learning to Rank.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020

2018
Drug Selection via Joint Push and Learning to Rank.
CoRR, 2018

2015
Simplicity of Kmeans Versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015


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