Addressing Inverse Problems in Frame Restoration with Siamese Conditional Variational Autoencoders
Keywords: autoencoder, inverse problem, Siamese networks
TL;DR: The paper introduces SMCVAE, using Siamese networks to restore missing information in video frames effectively.
Abstract: Restoring missing information in video frames is a challenging inverse problem, particularly in applications such as autonomous driving and surveillance. This paper introduces the Siamese Masked Conditional Variational Autoencoder (SMCVAE), a novel model that utilizes a Siamese network architecture with Siamese Vision Transformer (SiamViT) encoders. By leveraging the inherent similarities between paired frames, SMCVAE enhances the model's ability to accurately reconstruct missing content. This approach effectively tackles the problem of missing patches—often resulting from camera malfunctions—through advanced variational inference techniques. Experimental results demonstrate SMCVAE's superior performance in restoring lost information, highlighting its potential to solve complex inverse problems in real-world environments.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 463
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