With the rapid development of deep learning, many deep learning-based approaches have
made great achievements in object detection tasks. It is generally known that deep learning …

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 …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

As the core building block of vision transformers, attention is a powerful tool to capture long-
range dependency. However, such power comes at a cost: it incurs a huge computation …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

This work investigates a simple yet powerful adapter for Vision Transformer (ViT). Unlike
recent visual transformers that introduce vision-specific inductive biases into their …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …

We present DINO (\textbf {D} ETR with\textbf {I} mproved de\textbf {N} oising anch\textbf {O} r
boxes), a state-of-the-art end-to-end object detector.% in this paper. DINO improves over …