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Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. This paper proposes a two-stage passive NLOS imaging approach, aimed at reconstructing high-quality complicated hidden scenes. In the first stage, we train a coarse reconstruction network based on the optimal transport principle and using vector quantization to learn discrete priors for projection image encoding.

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The document is the supplementary materials of the paper of "Towards Validating Face Editing Ability in Generative Models" to provide more quantitative and qualitative results complementing the main paper.

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Recent developments in self-supervised learning (SSL) have made it possible to learn data representations without the need for annotations.
, which enhances the performance of various downstream tasks.

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In this paper, we introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues and shows robustness against different noise patterns, enhancing its broad applicability. Our method comprises three modules: a Feature generator creating features maps, a Denoise-Net generating denoised but slightly blurry reference frames, and a Refine-Net re-introducing high-frequency details.

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Underwater images often suffer from degradation due to refraction, back-scattering, and absorption, resulting in color cast, blur, and limited visibility. Such degradation hampers higher-level computer vision applications in autonomous underwater vehicles. Existing methods for enhancing degraded images often fail to preserve fine edges and true colors. Hence, an effective pre-processing network is vital for underwater image enhancement. Addressing this need, we propose a frequency modulated deformable transformer network.

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This document provides supplementary material for the paper titled “Latent Enhancing AutoEncoder for Occluded Image Classification” submitted to the regular track of the ICIP 2024. This document consists of details of the architecture of the LEARN, illustration of improvements in inter-class differentiability in latent space for OccludedPASCAL3D+ dataset (hereafter referred to as Pascal), and detailed classification results.

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