97 citations · 167 across the 14 of their papers we have counts for
22 papers · 1 filter
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht +1
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and…
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
Laurynas Karazija, Subhabrata Choudhury, Iro Laina +2
We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision.…
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…
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina +1
Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled…
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
Subhabrata Choudhury, Iro Laina, Christian Rupprecht +1
Most of us are not experts in specific fields, such as ornithology. Nonetheless, we do have general image and language understanding capabilities that we use to match what we see t…
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…