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 …

In this work, we present SEEM, a promotable and interactive model for segmenting
everything everywhere all at once in an image. In SEEM, we propose a novel and versatile …

In this work, we introduce Semantic-SAM, an augmented image segmentation foundation for
segmenting and recognizing anything at desired granularities. Compared to the …

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

Abstract This paper presents LLaVA-Plus (Large Language and Vision Assistants that Plug
and Learn to Use Skills), a general-purpose multimodal assistant trained using an end-to …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

With the recent significant advancements in large multimodal models (LMMs), the
importance of their grounding capability in visual chat is increasingly recognized. Despite …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to
combine with the segment anything model (SAM). This integration enables the detection and …