no code implementations • 15 Dec 2022 • Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Laurent Itti, Vibhav Vineet
Finally, the third component creates a large-scale pseudo-labeled instance segmentation training dataset by compositing the foreground object masks onto the original and generated background images.
no code implementations • 4 Dec 2022 • Yunhao Ge, Jie Ren, Yuxiao Wang, Andrew Gallagher, Ming-Hsuan Yang, Laurent Itti, Hartwig Adam, Balaji Lakshminarayanan, Jiaping Zhao
We also show that our method improves across ImageNet shifted datasets and other model architectures such as LiT.
1 code implementation • 22 Jul 2022 • Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song, Xin Wang, Laurent Itti, Vibhav Vineet
However, existing approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts of random data variations, which is slow and is often suboptimal for the target domain.
1 code implementation • 19 Jul 2022 • Yunhao Ge, Yao Xiao, Zhi Xu, Xingrui Wang, Laurent Itti
We use human experiments to confirm that both HVE and humans predominantly use some specific features to support the classification of specific classes (e. g., texture is the dominant feature to distinguish a zebra from other quadrupeds, both for humans and HVE).
no code implementations • 20 Jun 2022 • Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Neel Joshi, Laurent Itti, Vibhav Vineet
For foreground object mask generation, we use a simple textual template with object class name as input to DALL-E to generate a diverse set of foreground images.
no code implementations • 13 Jun 2022 • Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.
no code implementations • 6 Dec 2021 • Yunhao Ge, Zhi Xu, Yao Xiao, Gan Xin, Yunkui Pang, Laurent Itti
(2) They lack convexity constraints, which is important for meaningfully manipulating specific attributes for downstream tasks.
no code implementations • 29 Sep 2021 • Yunhao Ge, Yao Xiao, Zhi Xu, Linwei Li, Ziyan Wu, Laurent Itti
Take image classification as an example, HNI visualizes the reasoning logic of a NN with class-specific Structural Concept Graphs (c-SCG), which are human-interpretable.
no code implementations • CVPR 2021 • Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, Ziyan Wu
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability.
1 code implementation • ICLR Workshop Neural_Compression 2021 • Yunhao Ge, Yunkui Pang, Linwei Li, Laurent Itti
We consider the problem of graph data compression and representation.
no code implementations • 1 Jan 2021 • Yunhao Ge, Gan Xin, Zhi Xu, Yao Xiao, Yunkui Pang, Yining HE, Laurent Itti
DEAE can become a generative model and synthesis semantic controllable samples by interpolating latent code, which can even synthesis novel attribute value never is shown in the original dataset.
no code implementations • 27 Sep 2020 • Shixian Wen, Amanda Rios, Yunhao Ge, Laurent Itti
Continual learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby a previously learned mapping of an old task is erased when learning new mappings for new tasks.
1 code implementation • ICLR 2021 • Yunhao Ge, Sami Abu-El-Haija, Gan Xin, Laurent Itti
Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc.
1 code implementation • ECCV 2020 • Yunhao Ge, Jiaping Zhao, Laurent Itti
After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality.
no code implementations • 30 Jul 2019 • Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang
Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.