Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

Given the large volume of remote sensing images collected daily, automatic object detection
and segmentation have been a consistent need in Earth observation (EO). However, objects …

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 …

Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

ChatGPT is attracting a cross-field interest as it provides a language interface with
remarkable conversational competency and reasoning capabilities across many domains …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

This paper presents a new framework for open-vocabulary semantic segmentation with the
pre-trained vision-language model, named SAN. Our approach models the semantic …

Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …