Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

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

The landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

Abstract This paper presents LLaVA-Plus (L arge L anguage a nd V ision A ssistants that P
lug and L earn to U se S kills), a general-purpose multimodal assistant trained using an end …

Training data for video segmentation are expensive to annotate. This impedes extensions of
end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary …

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 …

Open-vocabulary generalization requires robotic systems to perform tasks involving complex
and diverse environments and task goals. While the recent advances in vision language …

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 present T-Rex2, a highly practical model for open-set object detection. Previous open-
set object detection methods relying on text prompts effectively encapsulate the abstract …

Abstract The CLIP and Segment Anything Model (SAM) are remarkable vision foundation
models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is …