Yu-Xiong Wang

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Carnegie Mellon University

Few-Shot Learning with Intra-Class Knowledge Transfer

We consider the few-shot classification task with an unbalanced dataset,...
08/22/2020 ∙ by Vivek Roy, et al. ∙ 23 ∙ share

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Alpha Net: Adaptation with Composition in Classifier Space

Deep learning classification models typically train poorly on classes wi...
08/17/2020 ∙ by Nadine Chang, et al. ∙ 0 ∙ share

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Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

Generative modeling has recently shown great promise in computer vision,...
08/16/2020 ∙ by Zhipeng Bao, et al. ∙ 0 ∙ share

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Towards Streaming Image Understanding

Embodied perception refers to the ability of an autonomous agent to perc...
05/21/2020 ∙ by Mengtian Li, et al. ∙ 0 ∙ share

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Learning Generalizable Representations via Diverse Supervision

The problem of rare category recognition has received a lot of attention...
11/29/2019 ∙ by Ziqi Pang, et al. ∙ 9 ∙ share

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Growing a Brain: Fine-Tuning by Increasing Model Capacity

CNNs have made an undeniable impact on computer vision through the abili...
07/18/2019 ∙ by Yu-Xiong Wang, et al. ∙ 2 ∙ share

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Image Deformation Meta-Networks for One-Shot Learning

Humans can robustly learn novel visual concepts even when images undergo...
05/28/2019 ∙ by Zitian Chen, et al. ∙ 0 ∙ share

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Learning Compositional Representations for Few-Shot Recognition

One of the key limitations of modern deep learning based approaches lies...
12/21/2018 ∙ by Pavel Tokmakov, et al. ∙ 10 ∙ share

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Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can e...
01/16/2018 ∙ by Yu-Xiong Wang, et al. ∙ 0 ∙ share

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Yu-Xiong Wang

Featured Co-authors

Wei Liu

Ruslan Salakhutdinov

Yanwei Fu

Deva Ramanan

Ross Girshick

Lin Ma

Yan Xu

Martial Hebert

Bharath Hariharan

Kris Kitani

Pavel Tokmakov

Few-Shot Learning with Intra-Class Knowledge Transfer

Alpha Net: Adaptation with Composition in Classifier Space

Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

Towards Streaming Image Understanding

Learning Generalizable Representations via Diverse Supervision

Growing a Brain: Fine-Tuning by Increasing Model Capacity

Image Deformation Meta-Networks for One-Shot Learning

Learning Compositional Representations for Few-Shot Recognition

Low-Shot Learning from Imaginary Data