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 …

The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

We present ControlNet, a neural network architecture to add spatial conditioning controls to
large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large …

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 …

Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

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

Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

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