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07/25/2023
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review
Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously wh...
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06/12/2023
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation
Deep neural networks are vulnerable to adversarial examples. Adversarial...
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03/24/2023
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing
Deep neural networks can be easily fooled into making incorrect predicti...
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03/20/2023
Rethinking the backbone architecture for tiny object detection
Tiny object detection has become an active area of research because imag...
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01/24/2023
Data Augmentation Alone Can Improve Adversarial Training
Adversarial training suffers from the issue of robust overfitting, which...
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12/09/2022
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization
Adversarial training is widely used to improve the robustness of deep ne...
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10/27/2022
On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks
Initialising the synaptic weights of artificial neural networks (ANNs) w...
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10/21/2022
Query Semantic Reconstruction for Background in Few-Shot Segmentation
Few-shot segmentation (FSS) aims to segment unseen classes using a few a...
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07/15/2022
Registration based Few-Shot Anomaly Detection
This paper considers few-shot anomaly detection (FSAD), a practical yet ...
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07/31/2020