Large multimodal models (LMM) have recently shown encouraging progress with visual
instruction tuning. In this paper we present the first systematic study to investigate the design …

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

Large multimodal models demonstrate remarkable generalist ability to perform diverse
multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute …

General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

Big models, exemplified by Large Language Models (LLMs), are models typically pre-
trained on massive data and comprised of enormous parameters, which not only obtain …

Multimodal large language models acquire their instruction-following capabilities through a
two-stage training process: pre-training on image-text pairs and fine-tuning on supervised …

Instruction tuning is a crucial supervised training phase in Large Language Models (LLMs),
aiming to enhance the LLM's ability to generalize instruction execution and adapt to user …

We present Emu, a multimodal foundation model that seamlessly generates images and text
in multimodal context. This omnivore model can take in any single-modality or multimodal …

Large language models exhibit enhanced zero-shot performance on various tasks when fine-
tuned with instruction-following data. Multimodal instruction-following models extend these …