no code implementations • 21 Jan 2024 • Kiyoon Kim, Shreyank N Gowda, Panagiotis Eustratiadis, Antreas Antoniou, Robert B Fisher
More precisely, we created dataset splits of HMDB-51 or UCF-101 for training, and Kinetics-400 for testing, using the subset of the classes that are overlapping in both train and test datasets.
1 code implementation • 15 Jun 2023 • Panagiotis Eustratiadis, Łukasz Dudziak, Da Li, Timothy Hospedales
In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes.
1 code implementation • 1 Aug 2022 • Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss landscape that adversaries find difficult to navigate.
1 code implementation • 17 Oct 2020 • Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks.
1 code implementation • 26 May 2020 • Shreyank N Gowda, Panagiotis Eustratiadis, Timothy Hospedales, Laura Sevilla-Lara
We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time.