105 citations · 436 across the 29 of their papers we have counts for
22 papers · 1 filter
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Mohammadreza Salehi, Efstratios Gavves, Cees G. M. Snoek +1
Spatially dense self-supervised learning is a rapidly growing problem domain with promising applications for unsupervised segmentation and pretraining for dense downstream tasks. D…
Learning to Count without Annotations
Lukas Knobel, Tengda Han, Yuki M. Asano
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost a…
Self-Ordering Point Clouds
Pengwan Yang, Cees G. M. Snoek, Yuki M. Asano
In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this cha…
Skip-Attention: Improving Vision Transformers by Paying Less Attention
Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano +2
This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these ope…
VTC: Improving Video-Text Retrieval with User Comments
Laura Hanu, James Thewlis, Yuki M. Asano +1
Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and cons…
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers
Jochem Loedeman, Maarten C. Stol, Tengda Han +1
With the introduction of the transformer architecture in computer vision, increasing model scale has been demonstrated as a clear path to achieving performance and robustness gains…
Self-Guided Diffusion Models
Vincent Tao Hu, David W Zhang, Yuki M. Asano +2
Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process. However, guidance requires a…
Measuring the Interpretability of Unsupervised Representations via Quantized Reverse Probing
Iro Laina, Yuki M. Asano, Andrea Vedaldi
Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through trans…
Less than Few: Self-Shot Video Instance Segmentation
Pengwan Yang, Yuki M. Asano, Pascal Mettes +1
The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labell…
Self-Supervised Learning of Object Parts for Semantic Segmentation
Adrian Ziegler, Yuki M. Asano
Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks suc…
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…
Support-set bottlenecks for video-text representation learning
Mandela Patrick, Po-Yao Huang, Yuki Asano +4
The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to…
Labelling unlabelled videos from scratch with multi-modal self-supervision
Yuki M. Asano, Mandela Patrick, Christian Rupprecht +1
A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data. Yet, labelling a dataset with human annotation continues to…
On Compositions of Transformations in Contrastive Self-Supervised Learning
Mandela Patrick, Yuki M. Asano, Polina Kuznetsova +4
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we general…
Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill p…
A critical analysis of self-supervision, or what we can learn from a single image
Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi
We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative met…