Object counting methods typically rely on manually annotated datasets. The
cost of creating such datasets has restricted the versatility of these networks
to count objects from specific classes (such as humans or penguins), and
counting objects from diverse categories remains a challenge. The availability
of robust text-to-image latent diffusion models (LDMs) raises the question of
whether these models can be utilized to generate counting datasets. However,
LDMs struggle to create images with an exact number of objects based solely on
text prompts but they can be used to offer a dependable \textit{sorting} signal
by adding and removing objects within an image. Leveraging this data, we
initially introduce an unsupervised sorting methodology to learn object-related
features that are subsequently refined and anchored for counting purposes using
counting data generated by LDMs. Further, we present a density
classifier-guided method for dividing an image into patches containing objects
that can be reliably counted. Consequently, we can generate counting data for
any type of object and count them in an unsupervised manner. Our approach
outperforms other unsupervised and few-shot alternatives and is not restricted
to specific object classes for which counting data is available. Code to be
released upon acceptance.

利用文本到图像的潜在扩散模型 (LDMs)，本研究介绍了一种无监督的排序方法，通过借助生成的计数数据，对任意类型的对象进行可靠计数，该方法优于其他无监督和少样本方法，不受特定对象类别计数数据的限制。

AFreeCA：无需标注的全能计数

AFreeCA: Annotation-Free Counting for All

We propose a new method for solving imaging inverse problems using
text-to-image latent diffusion models as general priors. Existing methods using
latent diffusion models for inverse problems typically rely on simple null text
prompts, which can lead to suboptimal performance. To address this limitation,
we introduce a method for prompt tuning, which jointly optimizes the text
embedding on-the-fly while running the reverse diffusion process. This allows
us to generate images that are more faithful to the diffusion prior. In
addition, we propose a method to keep the evolution of latent variables within
the range space of the encoder, by projection. This helps to reduce image
artifacts, a major problem when using latent diffusion models instead of
pixel-based diffusion models. Our combined method, called P2L, outperforms both
image- and latent-diffusion model-based inverse problem solvers on a variety of
tasks, such as super-resolution, deblurring, and inpainting.

我们提出了一种使用文本到图像潜在扩散模型作为通用先验来解决图像逆问题的新方法。我们引入了一种 prompt 调整方法，通过在运行反向扩散过程时动态优化文本嵌入，使得我们能够生成更符合扩散先验的图像。此外，我们提出了一种投影方法，以保持潜在变量在编码器的范围空间内的演化，从而有助于减少图像伪影问题。我们的综合方法 P2L 在各种任务（如超分辨率、去模糊和修复缺失部分）上优于基于图像和潜在扩散模型的逆问题求解器。