Remote sensing image change captioning (RSICC) aims to automatically generate
sentences that describe content differences in remote sensing bitemporal
images. Recently, attention-based transformers have become a prevalent idea for
capturing the features of global change. However, existing transformer-based
RSICC methods face challenges, e.g., high parameters and high computational
complexity caused by the self-attention operation in the transformer encoder
component. To alleviate these issues, this paper proposes a Sparse Focus
Transformer (SFT) for the RSICC task. Specifically, the SFT network consists of
three main components, i.e. a high-level features extractor based on a
convolutional neural network (CNN), a sparse focus attention mechanism-based
transformer encoder network designed to locate and capture changing regions in
dual-temporal images, and a description decoder that embeds images and words to
generate sentences for captioning differences. The proposed SFT network can
reduce the parameter number and computational complexity by incorporating a
sparse attention mechanism within the transformer encoder network. Experimental
results on various datasets demonstrate that even with a reduction of over 90\%
in parameters and computational complexity for the transformer encoder, our
proposed network can still obtain competitive performance compared to other
state-of-the-art RSICC methods. The code can be available at

提出了一种稀疏聚焦变换器用于遥感图像变化编码任务，通过在变换器编码中引入稀疏注意机制，降低参数数量和计算复杂度，与其他最先进的遥感图像变化编码方法相比，仍能获得竞争性性能。

一种用于遥感图像变化描述的轻量级 Transformer

A Lightweight Transformer for Remote Sensing Image Change Captioning

With the rise in manipulated media, deepfake detection has become an
imperative task for preserving the authenticity of digital content. In this
paper, we present a novel multi-modal audio-video framework designed to
concurrently process audio and video inputs for deepfake detection tasks. Our
model capitalizes on lip synchronization with input audio through a
cross-attention mechanism while extracting visual cues via a fine-tuned VGG-16
network. Subsequently, a transformer encoder network is employed to perform
facial self-attention. We conduct multiple ablation studies highlighting
different strengths of our approach. Our multi-modal methodology outperforms
state-of-the-art multi-modal deepfake detection techniques in terms of F-1 and
per-video AUC scores.

通过一个新颖的多模态音视频框架，结合音频和视频输入进行深度伪造检测，我们的模型利用交叉注意机制通过嘴唇同步输入音频，并通过经过微调的 VGG-16 网络提取视觉线索，随后利用 Transformer 编码器网络执行面部自注意力。我们进行了多个消融研究，突出了我们方法的不同优点。我们的多模态方法在 F-1 分数和每个视频 AUC 分数方面优于最先进的多模态深度伪造检测技术。