In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Abstract The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …

We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

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 …

The vision-based perception for autonomous driving has undergone a transformation from
the bird-eye-view (BEV) representations to the 3D semantic occupancy. Compared with the …

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 …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …