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06/10/2022
Masked Autoencoders are Robust Data Augmentors
Deep neural networks are capable of learning powerful representations to...
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11/25/2021
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learni...
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09/30/2021
Motion-aware Self-supervised Video Representation Learning via Foreground-background Merging
In light of the success of contrastive learning in the image domain, cur...
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07/04/2021
Bag of Instances Aggregation Boosts Self-supervised Learning
Recent advances in self-supervised learning have experienced remarkable ...
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06/08/2021
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
Collecting annotated data for semantic segmentation is time-consuming an...
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05/16/2021
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive un...
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12/04/2020
Hierarchical Semantic Aggregation for Contrastive Representation Learning
Self-supervised learning based on instance discrimination has shown rema...
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07/27/2020
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
In this paper, we propose the K-Shot Contrastive Learning (KSCL) of visu...
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12/29/2019
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
One of the most significant challenges facing a few-shot learning task i...
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11/16/2019