Building generalized models that can solve many computer vision tasks
simultaneously is an intriguing direction. Recent works have shown image itself
can be used as a natural interface for general-purpose visual perception and
demonstrated inspiring results. In this paper, we explore diffusion-based
vision generalists, where we unify different types of dense prediction tasks as
conditional image generation and re-purpose pre-trained diffusion models for
it. However, directly applying off-the-shelf latent diffusion models leads to a
quantization issue. Thus, we propose to perform diffusion in pixel space and
provide a recipe for finetuning pre-trained text-to-image diffusion models for
dense vision tasks. In experiments, we evaluate our method on four different
types of tasks and show competitive performance to the other vision
generalists.

构建广义模型以同时解决多个计算机视觉任务是一个有趣的方向，本文探索基于扩散的视觉通用模型，将不同类型的密集预测任务统一为条件图像生成，并重新调整预训练的扩散模型，我们在四种不同类型的任务上实验证明了与其他视觉通用模型竞争性的性能。

面向稠密视觉任务的扩散型通用模型

Toward a Diffusion-Based Generalist for Dense Vision Tasks

Fashion merchandising is one of the most complicated problems in forecasting,
given the transient nature of trends in colours, prints, cuts, patterns, and
materials in fashion, the economies of scale achievable only in bulk
production, as well as geographical variations in consumption. Retailers that
serve a large customer base spend a lot of money and resources to stay prepared
for meeting changing fashion demands, and incur huge losses in unsold inventory
and liquidation costs [2]. This problem has been addressed by analysts and
statisticians as well as ML researchers in a conventional fashion - of building
models that forecast for future demand given a particular item of fashion with
historical data on its sales. To our knowledge, none of these models have
generalized well to predict future demand at an abstracted level for a new
design/style of fashion article. To address this problem, we present a study of
large scale fashion sales data and directly infer which clothing/footwear
attributes and merchandising factors drove demand for those items. We then
build generalised models to forecast demand given new item attributes, and
demonstrate robust performance by experimenting with different neural
architectures, ML methods, and loss functions.

通过对大规模时尚销售数据的分析，建立了可预测未来需要的一般化模型，并演示了与不同神经结构、机器学习方法和损失函数的实验结果表现良好。