Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Today's deep learning methods focus on how to design the objective functions to make the
prediction as close as possible to the target. Meanwhile, an appropriate neural network …

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 …

The" Roaring 20s" of visual recognition began with the introduction of Vision Transformers
(ViTs), which quickly superseded ConvNets as the state-of-the-art image classification …

Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …

Text-to-image generation has traditionally focused on finding better modeling assumptions
for training on a fixed dataset. These assumptions might involve complex architectures …

Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …

In this paper, we propose a conceptually simple but very effective attention module for
Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …

In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for
image and video classification, as well as object detection. We present an improved version …