Xialei Liu

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Universitat Autònoma de Barcelona

Continual Universal Object Detection

Object detection has improved significantly in recent years on multiple ...
02/13/2020 ∙ by Xialei Liu, et al. ∙ 33 ∙ share

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Memory Replay GANs: learning to generate images from new categories without forgetting

Previous works on sequential learning address the problem of forgetting ...
09/06/2018 ∙ by Chenshen Wu, et al. ∙ 12 ∙ share

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RankIQA: Learning from Rankings for No-reference Image Quality Assessment

We propose a no-reference image quality assessment (NR-IQA) approach tha...
07/26/2017 ∙ by Xialei Liu, et al. ∙ 0 ∙ share

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Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

In this paper we propose an approach to avoiding catastrophic forgetting...
02/08/2018 ∙ by Xialei Liu, et al. ∙ 0 ∙ share

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Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

We propose a novel crowd counting approach that leverages abundantly ava...
03/08/2018 ∙ by Xialei Liu, et al. ∙ 0 ∙ share

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Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

For many applications the collection of labeled data is expensive labori...
02/17/2019 ∙ by Xialei Liu, et al. ∙ 0 ∙ share

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Learning Metrics from Teachers: Compact Networks for Image Embedding

Metric learning networks are used to compute image embeddings, which are...
04/07/2019 ∙ by Lu Yu, et al. ∙ 0 ∙ share

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Semantic Drift Compensation for Class-Incremental Learning

Class-incremental learning of deep networks sequentially increases the n...
04/01/2020 ∙ by Lu Yu, et al. ∙ 0 ∙ share

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Generative Feature Replay For Class-Incremental Learning

Humans are capable of learning new tasks without forgetting previous one...
04/20/2020 ∙ by Xialei Liu, et al. ∙ 0 ∙ share

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Xialei Liu

Featured Co-authors

Stefano Soatto

Joost van de Weijer

Kai Wang

Hao Yang

Antonio M. Lopez

Luis Herranz

Yaxing Wang

Andrew D. Bagdanov

Marc Masana

Lu Yu

Bogdan Raducanu

Continual Universal Object Detection

Memory Replay GANs: learning to generate images from new categories without forgetting

RankIQA: Learning from Rankings for No-reference Image Quality Assessment

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

Learning Metrics from Teachers: Compact Networks for Image Embedding

Semantic Drift Compensation for Class-Incremental Learning

Generative Feature Replay For Class-Incremental Learning