Remote sensing image change captioning (RSICC) aims at generating human-like
language to describe the semantic changes between bi-temporal remote sensing
image pairs. It provides valuable insights into environmental dynamics and land
management. Unlike conventional change captioning task, RSICC involves not only
retrieving relevant information across different modalities and generating
fluent captions, but also mitigating the impact of pixel-level differences on
terrain change localization. The pixel problem due to long time span decreases
the accuracy of generated caption. Inspired by the remarkable generative power
of diffusion model, we propose a probabilistic diffusion model for RSICC to
solve the aforementioned problems. In training process, we construct a noise
predictor conditioned on cross modal features to learn the distribution from
the real caption distribution to the standard Gaussian distribution under the
Markov chain. Meanwhile, a cross-mode fusion and a stacking self-attention
module are designed for noise predictor in the reverse process. In testing
phase, the well-trained noise predictor helps to estimate the mean value of the
distribution and generate change captions step by step. Extensive experiments
on the LEVIR-CC dataset demonstrate the effectiveness of our Diffusion-RSCC and
its individual components. The quantitative results showcase superior
performance over existing methods across both traditional and newly augmented
metrics. The code and materials will be available online at
this https URL

遥感图像变化标题生成（RSICC）旨在生成人类化语言，描述双时相遥感图像对之间的语义变化。我们提出了一种概率扩散模型来解决传统变化标题生成任务中的像素级差异对地形变化定位的影响，并在 LEVIR-CC 数据集上进行了广泛实验，展示了我们 Diffusion-RSCC 的有效性及其各个组成部分。

Diffusion-RSCC: 遥感图像中变化描述的扩散概率模型

Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in  Remote Sensing Images

Deep learning (DL) has successfully automated dose distribution prediction in
radiotherapy planning, enhancing both efficiency and quality. However, existing
methods suffer from the over-smoothing problem for their commonly used L1 or L2
loss with posterior average calculations. To alleviate this limitation, we
propose a diffusion model-based method (DiffDose) for predicting the
radiotherapy dose distribution of cancer patients. Specifically, the DiffDose
model contains a forward process and a reverse process. In the forward process,
DiffDose transforms dose distribution maps into pure Gaussian noise by
gradually adding small noise and a noise predictor is simultaneously trained to
estimate the noise added at each timestep. In the reverse process, it removes
the noise from the pure Gaussian noise in multiple steps with the well-trained
noise predictor and finally outputs the predicted dose distribution maps...

通过扩散模型为癌症患者的放射治疗剂量分布预测提出了一种方法 (DiffDose)，该方法通过正向和逆向过程分别将剂量分布转化为高斯噪音和去除噪音，从而有效地解决了现有方法中平滑问题和损失计算的限制。