Search Results for author: Yutong Zheng

Found 6 papers, 0 papers with code

Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling

no code implementations7 Apr 2022 Ran Tao, Han Zhang, Yutong Zheng, Marios Savvides

Class-agnostic bias is defined as the distribution shifting introduced by domain difference, which we propose Distribution Calibration Module(DCM) to reduce.

Few-Shot Learning

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement

no code implementations22 Aug 2019 Zhiqiang Shen, Zhankui He, Wanyun Cui, Jiahui Yu, Yutong Zheng, Chenchen Zhu, Marios Savvides

In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.

Knowledge Distillation

Ring loss: Convex Feature Normalization for Face Recognition

no code implementations CVPR 2018 Yutong Zheng, Dipan K. Pal, Marios Savvides

We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax.

Face Identification Face Recognition +1

Towards a Deep Learning Framework for Unconstrained Face Detection

no code implementations16 Dec 2016 Yutong Zheng, Chenchen Zhu, Khoa Luu, Chandrasekhar Bhagavatula, T. Hoang Ngan Le, Marios Savvides

Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc.

Face Detection Face Recognition +2

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

no code implementations17 Jun 2016 Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i. e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc.

Face Detection Face Recognition +5

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