Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost
sensors, the easy fusion of other sensors, and richer environmental information. Traditional …

A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

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 …

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 …

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …

We present X-Decoder, a generalized decoding model that can predict pixel-level
segmentation and language tokens seamlessly. X-Decoder takes as input two types of …

All instance perception tasks aim at finding certain objects specified by some queries such
as category names, language expressions, and target annotations, but this complete field …

Traditional camera sensors rely on human eyes for observation. However, human eyes are
prone to fatigue when observing objects of different sizes for a long time in complex scenes …

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

We present SegGPT, a generalist model for segmenting everything in context. We unify
various segmentation tasks into a generalist in-context learning framework that …