Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

Object detection is one of the predominant and challenging problems in computer vision.
Over the decade, with the expeditious evolution of deep learning, researchers have …

The cost of vision-and-language pre-training has become increasingly prohibitive due to
end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

Instruction tuning large language models (LLMs) using machine-generated instruction-
following data has been shown to improve zero-shot capabilities on new tasks, but the idea …

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 …

For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of
training on large amounts of noisy image-text data, without relying on expensive accurate …

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated
strong power of learning complex structures and meaningful semantics. However, relying …

Large-scale pre-training and instruction tuning have been successful at creating general-
purpose language models with broad competence. However, building general-purpose …