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 Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-
language tasks, such as image captioning and question answering, but lack the essential …

Open-vocabulary image segmentation aims to partition an image into semantic regions
according to arbitrary text descriptions. However, complex visual scenes can be naturally …

We introduce Florence-2 a novel vision foundation model with a unified prompt-based
representation for various computer vision and vision-language tasks. While existing large …

We present GLEE in this work an object-level foundation model for locating and identifying
objects in images and videos. Through a unified framework GLEEaccomplishes detection …

This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that
compares favorably to similar models that are 10x larger. As part of arriving at this strong …

Referring image segmentation (RIS) aims to find a segmentation mask given a referring
expression grounded to a region of the input image. Collecting labelled datasets for this …

Abstract Referring Image Segmentation (RIS) leveraging transformers has achieved great
success on the interpretation of complex visual-language tasks. However, the quadratic …

Abstract Generalized Referring Expression Segmentation (GRES) extends the scope of
classic RES to refer to multiple objects in one expression or identify the empty targets absent …

The reference-based object segmentation tasks, namely referring image segmentation
(RIS), referring video object segmentation (RVOS), and video object segmentation (VOS) …