In this paper, we study the text-guided image generation task. Our focus lies
in the modification of a reference image, given user text feedback, to imbue it
with specific desired properties. Despite recent strides in this field, a
persistent challenge remains that single-round optimization often overlooks
crucial details, particularly in the realm of fine-grained changes like shoes
or sleeves. This misalignment accumulation significantly hampers multi-round
customization during interaction. In an attempt to address this challenge, we
introduce a new self-supervised regularization into the existing framework,
i.e., multi-round regularization. It builds upon the observation that the
modification order does not affect the final result. As the name suggests, the
multi-round regularization encourages the model to maintain consistency across
different modification orders. Specifically, our proposed approach addresses
the issue where an initial failure to capture fine-grained details leads to
substantial discrepancies after multiple rounds, as opposed to traditional
one-round learning. Both qualitative and quantitative experiments show the
proposed method achieves high-fidelity generation quality over the text-guided
generation task, especially the local modification. Furthermore, we extend the
evaluation to semantic alignment with text by applying our method to
text-guided retrieval datasets, such as FahisonIQ, where it demonstrates
competitive performance.

本研究针对文本引导的图像生成任务，通过引入多轮规则化方法解决了单次优化忽略细节的问题，特别是在细微变化方面。我们的方法在文本引导的生成任务中展现了高保真度的生成质量，并在与文本的语义匹配方面得到了竞争性的表现。

培养多轮思维的文本引导图像生成

Instilling Multi-round Thinking to Text-guided Image Generation

Real-time and accurate information on fine-grained changes in crop
cultivation is of great significance for crop growth monitoring, yield
prediction and agricultural structure adjustment. Aiming at the problems of
serious spectral confusion in visible high-resolution unmanned aerial vehicle
(UAV) images of different phases, interference of large complex background and
salt-and-pepper noise by existing semantic change detection (SCD) algorithms,
in order to effectively extract deep image features of crops and meet the
demand of agricultural practical engineering applications, this paper designs
and proposes an agricultural geographic scene and parcel-scale constrained SCD
framework for crops (AGSPNet). AGSPNet framework contains three parts:
agricultural geographic scene (AGS) division module, parcel edge extraction
module and crop SCD module. Meanwhile, we produce and introduce an UAV image
SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple
semantic variation types of crops in complex geographical scene. We conduct
comparative experiments and accuracy evaluations in two test areas of this
dataset, and the results show that the crop SCD results of AGSPNet consistently
outperform other deep learning SCD models in terms of quantity and quality,
with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining
improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over
the sub-optimal method. The method proposed in this paper can clearly detect
the fine-grained change information of crop types in complex scenes, which can
provide scientific and technical support for smart agriculture monitoring and
management, food policy formulation and food security assurance.

该研究提出了一种针对农作物的农业地理场景和地块尺度受限的语义变化检测（SCD）框架（AGSPNet），通过有效提取农作物的深度图像特征，可以在复杂的场景中明确检测到农作物类型的细微变化信息，从而为智能农业监测与管理、粮食政策制定和食品安全保障提供科学和技术支持。

AGSPNet: 一种基于农业地理场景约束的无人机高分辨率图像农作物细粒度语义变化检测框架

AGSPNet: A framework for parcel-scale crop fine-grained semantic change  detection from UAV high-resolution imagery with agricultural geographic scene  constraints