( en | de | ru )

 

Disambiguation of "Qi, Guo-Jun"

Author of the publication

copydeleteadd this publication to your clipboard
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups.

, , , , and . CoRR, (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Qi Guo

Guo Jun

Jun-qi Liu

Guo-Jun Diao

Qi Qi

 

Other publications of authors with the same name

Rethinking the Combined and Individual Orders of Derivative of States for Differential Recurrent Neural Networks: Deep Differential Recurrent Neural Networks., , , and . ACM Trans. Multim. Comput. Commun. Appl., 15 (3): 83:1-83:21 (2019)E-Optimal Sensor Selection for Compressive Sensing-Based Purposes., , , , and . IEEE Trans. Big Data, 6 (1): 51-65 (2020)Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods., and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (4): 2168-2187 (2022)Contrastive Learning With Stronger Augmentations., and . IEEE Trans. Pattern Anal. Mach. Intell., 45 (5): 5549-5560 (May 2023)Efficient semantic annotation method for indexing large personal video database., , , , , and . Multimedia Information Retrieval, page 289-296. ACM, (2006)FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER., , , , and . CVPR, page 13945-13954. IEEE, (2023)AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations Rather Than Data., , , and . CVPR, Computer Vision Foundation / IEEE, (2019)CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces., , and . NeurIPS, page 5819-5828. (2018)Microsoft Research Asia TRECVID 2006 High-Level Feature Extraction and Rushes Exploitation., , , , , , , and . TRECVID, National Institute of Standards and Technology (NIST), (2006)Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting., , , and . KDD, page 987-995. ACM, (2017)