Change detection, as a research hotspot in the field of remote sensing, has
witnessed continuous development and progress. However, the discrimination of
boundary details remains a significant bottleneck due to the complexity of
surrounding elements between change areas and backgrounds. Discriminating the
boundaries of large change areas results in misalignment, while connecting
boundaries occurs for small change targets. To address the above issues, a
novel network based on the localization-then-refinement strategy is proposed in
this paper, namely LRNet. LRNet consists of two stages: localization and
refinement. In the localization stage, a three-branch encoder simultaneously
extracts original image features and their differential features for
interactive localization of the position of each change area. To minimize
information loss during feature extraction, learnable optimal pooling (LOP) is
proposed to replace the widely used max-pooling. Additionally, this process is
trainable and contributes to the overall optimization of the network. To
effectively interact features from different branches and accurately locate
change areas of various sizes, change alignment attention (C2A) and
hierarchical change alignment module (HCA) are proposed. In the refinement
stage, the localization results from the localization stage are corrected by
constraining the change areas and change edges through the edge-area alignment
module (E2A). Subsequently, the decoder, combined with the difference features
strengthened by C2A in the localization phase, refines change areas of
different sizes, ultimately achieving accurate boundary discrimination of
change areas. The proposed LRNet outperforms 13 other state-of-the-art methods
in terms of comprehensive evaluation metrics and provides the most precise
boundary discrimination results on the LEVIR-CD and WHU-CD datasets.

本文提出了基于定位再细化策略的新型网络 LRNet，通过定位和细化阶段，使用学习优化池化（LOP）和变换对齐注意力（C2A）等方法，提高了改变区域的定位和边界判别准确性，并在 LEVIR-CD 和 WHU-CD 数据集上取得了最精确的边界判别结果。

LRNet: 高分辨率遥感图像的变化检测基于定位优化策略

LRNet: Change detection of high-resolution remote sensing imagery via  strategy of localization-then-refinement

Deepfakes is a branch of malicious techniques that transplant a target face
to the original one in videos, resulting in serious problems such as
infringement of copyright, confusion of information, or even public panic.
Previous efforts for Deepfakes videos detection mainly focused on appearance
features, which have a risk of being bypassed by sophisticated manipulation,
also resulting in high model complexity and sensitiveness to noise. Besides,
how to mine the temporal features of manipulated videos and exploit them is
still an open question. We propose an efficient and robust framework named
LRNet for detecting Deepfakes videos through temporal modeling on precise
geometric features. A novel calibration module is devised to enhance the
precision of geometric features, making it more discriminative, and a
two-stream Recurrent Neural Network (RNN) is constructed for sufficient
exploitation of temporal features. Compared to previous methods, our proposed
method is lighter-weighted and easier to train. Moreover, our method has shown
robustness in detecting highly compressed or noise corrupted videos. Our model
achieved 0.999 AUC on FaceForensics++ dataset. Meanwhile, it has a graceful
decline in performance (-0.042 AUC) when faced with highly compressed videos.

LRNet 是一种有效且鲁棒的检测 Deepfakes 视频的框架，通过对精确几何特征的时间建模来挖掘深度伪造视频的时间特征，并对其进行有效利用，相较于以往的方法更轻量级且更易于训练，在高度压缩或噪声受损的情况下依然具有鲁棒性。