Search Results for author: Yuki M. Asano

Found 11 papers, 7 papers with code

PASS: An ImageNet replacement for self-supervised pretraining without humans

1 code implementation27 Sep 2021 Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, Andrea Vedaldi

On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining.

Pose Estimation Transfer Learning

Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning

1 code implementation ICCV 2021 Mandela Patrick, Yuki M. Asano, Bernie Huang, Ishan Misra, Florian Metze, Joao Henriques, Andrea Vedaldi

First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well.

Representation Learning Self-Supervised Learning

Privacy-preserving Object Detection

no code implementations11 Mar 2021 Peiyang He, Charlie Griffin, Krzysztof Kacprzyk, Artjom Joosen, Michael Collyer, Aleksandar Shtedritski, Yuki M. Asano

Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face.

Object Detection

Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models

1 code implementation8 Feb 2021 Hannah Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frederic A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano

Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in US Labour Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations.

Language Modelling Text Generation

Cannot find the paper you are looking for? You can Submit a new open access paper.