Xialei Liu

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Nankai University

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Universitat Autònoma de Barcelona

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∙ 11/29/2021

Learning Multiple Dense Prediction Tasks from Partially Annotated Data

Despite the recent advances in multi-task learning of dense prediction p...

Wei-Hong Li, et al. ∙

∙ 11/09/2021

Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition

Most meta-learning approaches assume the existence of a very large set o...

Kai Wang, et al. ∙

∙ 10/21/2021

HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification

Human beings learn and accumulate hierarchical knowledge over their life...

Kai Wang, et al. ∙

∙ 07/01/2021

Improving Task Adaptation for Cross-domain Few-shot Learning

In this paper, we look at the problem of cross-domain few-shot classific...

Wei-Hong Li, et al. ∙

∙ 03/25/2021

Universal Representation Learning from Multiple Domains for Few-shot Classification

In this paper, we look at the problem of few-shot classification that ai...

Wei-Hong Li, et al. ∙

∙ 12/06/2020

Self-Training for Class-Incremental Semantic Segmentation

We study incremental learning for semantic segmentation where when learn...

Lu Yu, et al. ∙

∙ 07/31/2020

Learning to Rank for Active Learning: A Listwise Approach

Active learning emerged as an alternative to alleviate the effort to lab...

Minghan Li, et al. ∙

∙ 04/20/2020

Generative Feature Replay For Class-Incremental Learning

Humans are capable of learning new tasks without forgetting previous one...

Xialei Liu, et al. ∙

∙ 04/01/2020

Semantic Drift Compensation for Class-Incremental Learning

Class-incremental learning of deep networks sequentially increases the n...

Lu Yu, et al. ∙

∙ 02/13/2020

Continual Universal Object Detection

Object detection has improved significantly in recent years on multiple ...

Xialei Liu, et al. ∙

∙ 04/07/2019

Learning Metrics from Teachers: Compact Networks for Image Embedding

Metric learning networks are used to compute image embeddings, which are...

Lu Yu, et al. ∙

∙ 02/17/2019

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

For many applications the collection of labeled data is expensive labori...

Xialei Liu, et al. ∙

∙ 09/06/2018

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

Previous works on sequential learning address the problem of forgetting ...

Chenshen Wu, et al. ∙

∙ 03/08/2018

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

We propose a novel crowd counting approach that leverages abundantly ava...

Xialei Liu, et al. ∙

∙ 02/08/2018

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

In this paper we propose an approach to avoiding catastrophic forgetting...

Xialei Liu, et al. ∙

∙ 07/26/2017

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

We propose a no-reference image quality assessment (NR-IQA) approach tha...

Xialei Liu, et al. ∙