no code implementations • 27 Nov 2024 • Zhixu Tao, Ian Mason, Sanjeev Kulkarni, Xavier Boix
In this paper, we examine Task Arithmetic for multi-task learning by framing it as a one-shot Federated Learning problem.
no code implementations • 3 Oct 2024 • Avi Cooper, Keizo Kato, Chia-Hsien Shih, Hiroaki Yamane, Kasper Vinken, Kentaro Takemoto, Taro Sunagawa, Hao-Wei Yeh, Jin Yamanaka, Ian Mason, Xavier Boix
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness.
1 code implementation • 15 Jun 2023 • Ian Mason, Anirban Sarkar, Tomotake Sasaki, Xavier Boix
In this work we develop a compositional image classification task where, given a few elemental corruptions, models are asked to generalize to compositions of these corruptions.
no code implementations • 17 Mar 2023 • Anirban Sarkar, Matthew Groth, Ian Mason, Tomotake Sasaki, Xavier Boix
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios.
1 code implementation • ACM Transactions on Graphics 2022 • Sebastian Starke, Ian Mason, Taku Komura
Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis.
1 code implementation • 12 Jan 2022 • Ian Mason, Sebastian Starke, Taku Komura
In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases.
no code implementations • NeurIPS Workshop ICBINB 2021 • Cian Eastwood, Ian Mason, Chris Williams
To adapt to changes in real-world data distributions, neural networks must update their parameters.
1 code implementation • ICLR 2022 • Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf
Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain.