A number of recent works have proposed attention models for Visual Question Answering (VQA)
that generate spatial maps highlighting image regions relevant to answering the …

This paper presents a detailed study of improving vision features and develops an improved
object detection model for vision language (VL) tasks. Compared to the most widely used …

Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive
results on image classification in both zero-shot and transfer learning settings. However, …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

We introduce a novel framework for image captioning that can produce natural language
explicitly grounded in entities that object detectors find in the image. Our approach reconciles …

This paper presents a grounded language-image pre-training (GLIP) model for learning
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …

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 decoding …

Automated visual understanding of our diverse and open world demands computer vision
models to generalize well with minimal customization for specific tasks, similar to human vision…

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 propose …

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 …