Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

We present Imagen, a text-to-image diffusion model with an unprecedented degree of
photorealism and a deep level of language understanding. Imagen builds on the power of …

Image-based visual-language (I-VL) pre-training has shown great success for learning joint
visual-textual representations from large-scale web data, revealing remarkable ability for …

Videos are a rich source of multi-modal supervision. In this work, we learn representations
using self-supervision by leveraging three modalities naturally present in videos: visual …

The goal of this work is to establish a scalable pipeline for expanding an object detector
towards novel/unseen categories, using zero manual annotations. To achieve that, we make …

State-of-the-art natural language processing systems rely on supervision in the form of
annotated data to learn competent models. These models are generally trained on data in a …

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …