Multimodal

Recent multimodal models process both text and images as sequences of tokens, but they learn to represent these distinct data types using separate loss functions. Recent work unifies the loss function as well.

Get ready for the next wave of language-model mania. OpenAI introduced the latest in its GPT series of large language models to widespread excitement. The company showed statistics and examples designed to demonstrate...

Individual deep learning models proved their mettle in hundreds of tasks. The scope of multi-task models expanded dramatically in the past year.

Researchers took a step toward achieving a longstanding goal: One model that performs a whole lot of very different tasks. Scott Reed, Konrad Żołna, Emilio Parisotto and a team at DeepMind announced Gato.

Amazon reported long-term success using machine learning to shrink its environmental footprint. The online retailer developed a system that fuses product descriptions, images, and structured data to decide how an item should be packed for shipping.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

To understand a movie scene, viewers often must remember or infer previous events and extrapolate potential consequences. New work improved a model’s ability to do the same.

Google will upgrade its search engine with a new model that tracks the relationships between words, images, and, in time, videos — the first fruit of its latest research into multimodal machine learning and multilingual language modeling.

The emerging generation of trillion-parameter models needs datasets of billions of examples, but the most readily available source of examples on that scale — the web — is polluted with bias and antisocial expressions. A new study examines the issue.

The transformer architecture has shown an uncanny ability to model not only language but also images and proteins. New research found that it can apply what it learns from the first domain to the others.

Object detectors typically detect only items that were labeled in their training data. A new method liberates them to locate and recognize a much wider variety of objects.

Researchers have proposed task-agnostic architectures for image classification tasks and language tasks. New work proposes a single architecture for vision-language tasks.

Researchers unveiled competition for the reigning large language model GPT-3. Four models collectively called Wu Dao were described by Beijing Academy of Artificial Intelligence, a research collective funded by the Chinese government, according to Synced Review.

Models that interpret the interplay of words and images tend to be trained on richer bodies of text than images. Recent research worked toward giving such models a more balanced knowledge of the two domains.

An automated art critic spells out the emotional impact of images. Led by Panos Achlioptas, researchers at Ecole Polytechnique, King Abdullah University, and Stanford University trained a deep learning system to generate subjective interpretations of art.