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12/29/2022
Learning Multimodal Data Augmentation in Feature Space
The ability to jointly learn from multiple modalities, such as text, aud...
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12/15/2022
Are Multimodal Models Robust to Image and Text Perturbations?
Multimodal image-text models have shown remarkable performance in the pa...
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10/10/2022
Visual Prompt Tuning for Test-time Domain Adaptation
Models should have the ability to adapt to unseen data during test-time ...
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03/30/2021
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training dat...
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02/04/2021
SelfNorm and CrossNorm for Out-of-Distribution Robustness
Normalization techniques are crucial in stabilizing and accelerating the...
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07/17/2020
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Data augmentation is one of the most important tools in training modern ...
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03/01/2019
Learning where to look: Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the...
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08/20/2018
CU-Net: Coupled U-Nets
We design a new connectivity pattern for the U-Net architecture. Given s...
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08/07/2018
Quantized Densely Connected U-Nets for Efficient Landmark Localization
In this paper, we propose quantized densely connected U-Nets for efficie...
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05/24/2018
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Random data augmentation is a critical technique to avoid overfitting in...
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02/06/2018