Current deep networks are very data-hungry and benefit from training on large-scale
datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data …

The power of DNNs relies heavily on the quantity and quality of training data. However,
collecting and annotating data on a large scale is often expensive and time-consuming. To …

The advancement of visual intelligence is intrinsically tethered to the availability of large-
scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to …

We present a method for expanding a dataset by incorporating knowledge from the wide
distribution of pre-trained latent diffusion models. Data augmentations typically incorporate …

The scale and quality of a dataset significantly impact the performance of deep models.
However, acquiring large-scale annotated datasets is both a costly and time-consuming …

Diffusion models when conditioned on text prompts, generate realistic-looking images with
intricate details. But most of these pre-trained models fail to generate accurate images when …

In this chapter 1, we show that the expressiveness of an extremely simple query, the paired
comparison, is much greater than established in previous work. In the context of human …

The scale and quality of a dataset significantly impact the performance of deep models.
However, acquiring large-scale annotated datasets is both a costly and time-consuming …