Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Object detection is one of the predominant and challenging problems in computer vision.
Over the decade, with the expeditious evolution of deep learning, researchers have …

The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …

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 …

Cutting-edge diffusion models produce images with high quality and customizability,
enabling them to be used for commercial art and graphic design purposes. But do diffusion …

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 …

In this paper we present Mask DINO, a unified object detection and segmentation
framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by …

Contrastive language-image pre-training, CLIP for short, has gained increasing attention for
its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …