The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Generative modeling and representation learning are two key tasks in computer vision.
However, these models are typically trained independently, which ignores the potential for …

Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision
downstream tasks, but how and where MIM works remain unclear. In this paper, we compare …

We introduce SynCLR a novel approach for learning visual representations exclusively from
synthetic images without any real data. We synthesize a large dataset of image captions …

This paper revisits the standard pretrain-then-finetune paradigm used in computer vision for
visual recognition tasks. Typically, state-of-the-art foundation models are pretrained using …

Masked image modeling (MIM) has achieved promising results on various vision tasks.
However, the limited discriminability of learned representation manifests there is still plenty …

Transformers and masked language modeling are quickly being adopted and explored in
computer vision as vision transformers and masked image modeling (MIM). In this work, we …

In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …