Many online action prediction models observe complete frames to locate a...
Most existing OCR methods focus on alphanumeric characters due to the
po...
Few-shot learning (FSL) methods typically assume clean support sets with...
We study the challenging incremental few-shot object detection (iFSD)
se...
In many applications, such as autonomous driving, hand manipulation, or ...
Federated learning has emerged as an important distributed learning para...
Recent advances in OCR have shown that an end-to-end (E2E) training pipe...
As neural networks are increasingly being applied to real-world applicat...
We design blackbox transfer-based targeted adversarial attacks for an
en...
Large-scale language models have recently demonstrated impressive empiri...
Recently, progress has been made in the supervised training of Convoluti...
In domains where data are sensitive or private, there is great value in
...
We consider the blackbox transfer-based targeted adversarial attack thre...
Almost all current adversarial attacks of CNN classifiers rely on inform...
Naively trained neural networks tend to experience catastrophic forgetti...
As with other deep learning methods, label quality is important for lear...
For the safety of the traveling public, the Transportation Security
Admi...
We investigate time-dependent data analysis from the perspective of recu...
Generative Adversarial Networks (GANs) have proven to be a powerful fram...