This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

In this paper, we present an open-set object detector, called Grounding DINO, by marrying
Transformer-based detector DINO with grounded pre-training, which can detect arbitrary …

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

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 …

Large-scale text-to-image diffusion models have made amazing advances. However, the
status quo is to use text input alone, which can impede controllability. In this work, we …

Combining simple architectures with large-scale pre-training has led to massive
improvements in image classification. For object detection, pre-training and scaling …

We present GLIPv2, a grounded VL understanding model, that serves both localization tasks
(eg, object detection, instance segmentation) and Vision-Language (VL) understanding …

In this work, we present OpenSeeD, a simple Open-vocabulary Segmentation and Detection
framework that learns from different segmentation and detection datasets. To bridge the gap …

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

Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …