Search Results for author: Yingtian Zou

Found 7 papers, 1 papers with code

Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate

no code implementations ICLR 2022 Yingtian Zou, Fusheng Liu, Qianxiao Li

In this paper, we study the effect of the adaptation learning rate in meta-learning with mixed linear regression.

Meta-Learning

Image-to-Video Generation via 3D Facial Dynamics

no code implementations31 May 2021 Xiaoguang Tu, Yingtian Zou, Jian Zhao, Wenjie Ai, Jian Dong, Yuan YAO, Zhikang Wang, Guodong Guo, Zhifeng Li, Wei Liu, Jiashi Feng

Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.

Video Generation Video Prediction

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

4 code implementations ICCV 2019 Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, Jiashi Feng

In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.

Few-Shot Semantic Segmentation Metric Learning +1

Panoptic Edge Detection

no code implementations3 Jun 2019 Yuan Hu, Yingtian Zou, Jiashi Feng

In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.

Edge Detection Object Detection +1

Hierarchical Meta Learning

no code implementations19 Apr 2019 Yingtian Zou, Jiashi Feng

Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.

Few-Shot Learning

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