4 accepted papers
The recent emergence of powerful Vision-Language models (VLMs) has significantly improved image captioning. Some of these models are extended to caption videos as well. However, their capabilities to understand complex scenes are limited, and the descriptions they provide for scenes tend to be overl…
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effective accuracies and attractive FLOP counts, the added complexity actually makes these transformers slower than their vani…
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classific…
In this paper, we present a general algorithmic framework for developing easily parallelizable/distributable Markov Chain Monte Carlo (MCMC) algorithms. Our framework relies on the introduction of an auxiliary distribution called a ’shepherding distribution’ (SD) that is used to control several MCM…