105 citations · 436 across the 29 of their papers we have counts for
8 papers · 1 filter
The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image
Yuki M. Asano, Aaqib Saeed
What can neural networks learn about the visual world when provided with only a single image as input? While any image obviously cannot contain the multitudes of all existing objec…
PASS: An ImageNet replacement for self-supervised pretraining without humans
Yuki M. Asano, Christian Rupprecht, Andrew Zisserman +1
Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical…
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
Hannah Rose Kirk, Yennie Jun, Paulius Rauba +7
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released…
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Mandela Patrick, Dylan Campbell, Yuki M. Asano +5
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions. However, in a scene where objects or the camera may move, a physical point…
Self-supervised object detection from audio-visual correspondence
Triantafyllos Afouras, Yuki M. Asano, Francois Fagan +2
We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we…
Privacy-preserving Object Detection
Peiyang He, Charlie Griffin, Krzysztof Kacprzyk +4
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to p…
Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning
Mandela Patrick, Yuki M. Asano, Bernie Huang +4
The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers h…
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Hannah Kirk, Yennie Jun, Haider Iqbal +5
The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these…