With the availability of powerful text-to-image diffusion models, recent
works have explored the use of synthetic data to improve image classification
performances. These works show that it can effectively augment or even replace
real data. In this work, we investigate how synthetic data can benefit few-shot
class-agnostic counting. This requires to generate images that correspond to a
given input number of objects. However, text-to-image models struggle to grasp
the notion of count. We propose to rely on a double conditioning of Stable
Diffusion with both a prompt and a density map in order to augment a training
dataset for few-shot counting. Due to the small dataset size, the fine-tuned
model tends to generate images close to the training images. We propose to
enhance the diversity of synthesized images by exchanging captions between
images thus creating unseen configurations of object types and spatial layout.
Our experiments show that our diversified generation strategy significantly
improves the counting accuracy of two recent and performing few-shot counting
models on FSC147 and CARPK.

通过使用合成数据和多样化生成策略，本研究探讨了合成数据对于少样本计数问题中的作用，并显示其显著改进了两种最近的高性能少样本计数模型在 FSC147 和 CARPK 数据集上的计数准确性。

语义生成增强在少样本计数中的应用

Semantic Generative Augmentations for Few-Shot Counting

In this paper, we consider the problem of generalised visual object counting,
with the goal of developing a computational model for counting the number of
objects from arbitrary semantic categories, using arbitrary number of
"exemplars", i.e. zero-shot or few-shot counting. To this end, we make the
following four contributions: (1) We introduce a novel transformer-based
architecture for generalised visual object counting, termed as Counting
Transformer (CounTR), which explicitly capture the similarity between image
patches or with given "exemplars" with the attention mechanism;(2) We adopt a
two-stage training regime, that first pre-trains the model with self-supervised
learning, and followed by supervised fine-tuning;(3) We propose a simple,
scalable pipeline for synthesizing training images with a large number of
instances or that from different semantic categories, explicitly forcing the
model to make use of the given "exemplars";(4) We conduct thorough ablation
studies on the large-scale counting benchmark, e.g. FSC-147, and demonstrate
state-of-the-art performance on both zero and few-shot settings.

本研究提出了 Counting Transformer (CounTR) 用于实现对任意语义类别的目标进行计数，并采用两阶段的训练策略进行无监督预训练和有监督微调，通过综合评估获得了当前最先进的零样本和少样本计数结果，并使用大规模计数基准进行了彻底的消融研究。