75 citations · 231 across the 12 of their papers we have counts for
3 papers · 1 filter
Synth: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
Sahand Sharifzadeh, Christos Kaplanis, Shreya Pathak +5
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs). In this work, we investigat…
SemPPL: Predicting pseudo-labels for better contrastive representations
Matko Bošnjak, Pierre H. Richemond, Nenad Tomasev +7
Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method…
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Nenad Tomasev, Ioana Bica, Brian McWilliams +4
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classificatio…