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08/24/2023
Masked Autoencoders are Efficient Class Incremental Learners
Class Incremental Learning (CIL) aims to sequentially learn new classes ...
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08/07/2023
Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising
Calibration-based methods have dominated RAW image denoising under extre...
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12/16/2022
Robust Saliency Guidance for Data-free Class Incremental Learning
Data-Free Class Incremental Learning (DFCIL) aims to sequentially learn ...
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10/04/2022
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of ...
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10/01/2022
Long-Tailed Class Incremental Learning
In class incremental learning (CIL) a model must learn new classes in a ...
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04/06/2022
Universal Representations: A Unified Look at Multiple Task and Domain Learning
We propose a unified look at jointly learning multiple vision tasks and ...
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03/10/2022
Representation Compensation Networks for Continual Semantic Segmentation
In this work, we study the continual semantic segmentation problem, wher...
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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...
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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...
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10/21/2021
HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification
Human beings learn and accumulate hierarchical knowledge over their life...
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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...
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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...
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12/06/2020
Self-Training for Class-Incremental Semantic Segmentation
We study incremental learning for semantic segmentation where when learn...
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07/31/2020
Learning to Rank for Active Learning: A Listwise Approach
Active learning emerged as an alternative to alleviate the effort to lab...
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04/20/2020
Generative Feature Replay For Class-Incremental Learning
Humans are capable of learning new tasks without forgetting previous one...
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04/01/2020
Semantic Drift Compensation for Class-Incremental Learning
Class-incremental learning of deep networks sequentially increases the n...
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02/13/2020
Continual Universal Object Detection
Object detection has improved significantly in recent years on multiple ...
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04/07/2019
Learning Metrics from Teachers: Compact Networks for Image Embedding
Metric learning networks are used to compute image embeddings, which are...
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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...
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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 ...
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03/08/2018
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
We propose a novel crowd counting approach that leverages abundantly ava...
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02/08/2018
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
In this paper we propose an approach to avoiding catastrophic forgetting...
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07/26/2017
