Kangning Liu

According to our database, Kangning Liu authored at least 13 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Social Power Antecedents of Knowledge Sharing in Project-Oriented Online Communities.

Jianyao Jia, Guofeng Ma, Hongyang Li, Jiyong Ding, Kangning Liu

IEEE Trans. Engineering Management, 2024

2023

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.

Jinglian Tian, Yongzhong Tian, Yan Cao, Wenhao Wan, Kangning Liu

Sensors, July, 2023

Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects.

Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda

CoRR, 2023

Controllable One-Shot Face Video Synthesis With Semantic Aware Prior.

Kangning Liu, Yu-Chuan Su, Wei Hong, Ruijin Cang, Xuhui Jia

CoRR, 2023

Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning.

Kangning Liu, Weicheng Zhu, Yiqiu Shen, Sheng Liu, Narges Razavian, Krzysztof J. Geras, Carlos Fernandez-Granda

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Are All Losses Created Equal: A Neural Collapse Perspective.

Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

StrokeRehab: A Benchmark Dataset for Sub-second Action Identification.

Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi M. Schambra, Carlos Fernandez-Granda

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations.

Sheng Liu, Kangning Liu, Weicheng Zhu, Yiqiu Shen, Carlos Fernandez-Granda

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Kangning Liu, Sudarshini Tyagi, Laura Heacock, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Medical Image Anal., 2021

Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution.

Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi M. Schambra, Carlos Fernandez-Granda

CoRR, 2021

Cramér-Rao bound-informed training of neural networks for quantitative MRI.

Xiaoxia Zhang, Quentin Duchemin, Kangning Liu, Sebastian Flassbeck, Cem Gultekin, Carlos Fernandez-Granda, Jakob Assländer

CoRR, 2021

Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning.

Kangning Liu, Shuhang Gu, Andrés Romero, Radu Timofte

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis.

Kangning Liu, Yiqiu Shen, Nan Wu, Jakub Piotr Chledowski, Carlos Fernandez-Granda, Krzysztof J. Geras

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021