In the domain of computer vision, the restoration of missing information in
video frames is a critical challenge, particularly in applications such as
autonomous driving and surveillance systems. This paper introduces the Siamese
Masked Conditional Variational Autoencoder (SiamMCVAE), leveraging a siamese
architecture with twin encoders based on vision transformers. This innovative
design enhances the model's ability to comprehend lost content by capturing
intrinsic similarities between paired frames. SiamMCVAE proficiently
reconstructs missing elements in masked frames, effectively addressing issues
arising from camera malfunctions through variational inferences. Experimental
results robustly demonstrate the model's effectiveness in restoring missing
information, thus enhancing the resilience of computer vision systems. The
incorporation of Siamese Vision Transformer (SiamViT) encoders in SiamMCVAE
exemplifies promising potential for addressing real-world challenges in
computer vision, reinforcing the adaptability of autonomous systems in dynamic
environments.

该研究论文介绍了 Siamese Masked Conditional Variational Autoencoder（SiamMCVAE）技术，利用基于视觉变换器的孪生编码器的孪生架构。这一创新设计通过捕捉成对帧之间的内在相似性，增强了模型理解丢失内容的能力。SiamMCVAE 能够高效地重建蒙版帧中丢失的元素，通过变分推断有效地解决了由于相机故障引起的问题。实验证明了该模型在恢复缺失信息方面的有效性，从而提高了计算机视觉系统的韧性。在 SiamMCVAE 中引入 Siamese Vision Transformer (SiamViT) 编码器展示了应对计算机视觉领域现实挑战的潜力，增强了自主系统在动态环境中的适应能力。