no code implementations • 11 Mar 2024 • Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu
To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization.
1 code implementation • 27 Dec 2022 • Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.
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.
no code implementations • 31 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.
5 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.
Ranked #70 on Few-Shot Semantic Segmentation on COCO-20i (5-shot)
no code implementations • 3 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.
no code implementations • 19 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.
no code implementations • 7 Jan 2019 • Guohao Ying, Yingtian Zou, Lin Wan, Yiming Hu, Jiashi Feng
In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.
no code implementations • 16 Jul 2018 • Xinxing Su, Yingtian Zou, Yu Cheng, Shuangjie Xu, Mo Yu, Pan Zhou
We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem.