Sorry, you need to enable JavaScript to visit this website.

Supp ro TASE 2025 submission

This paper presents FaceLiVT, a lightweight yet powerful face recognition model that combines a hybrid CNN- Transformer architecture with an innovative and lightweight Multi-Head Linear Attention (MHLA) mechanism. By incorporating MHLA alongside a reparameterized token mixer, FaceLiVT effectively reduces computational complexity while preserving high accuracy. Extensive evaluations on challenging benchmarks—including LFW, CFP-FP, AgeDB-30, IJB-B, and IJB-C—highlight its superior performance compared to state-of-the-art lightweight models.

In this paper, we propose a novel approach, HDNet-LE, designed to enhance low-light (LL) images in terms of contrast, noise removal, and other degradation kinds by leveraging the power of Wavelet transform (WT) and Fourier Transform (FT). HDNet-LE combines the strengths of Generative Adversarial Networks (GAN) with the multi-scale analysis capabilities of the Wavelet transform and the spatial frequency domain through the FT. Experimental results demonstrate the effectiveness of the proposed method in improving the visibility and quality of LL images.

This document contains supplementary material for the ICIP PAPER.

This document contains supplementary material for the ICIP PAPER.

The video represents the Sheep-Sculpture rendering at 360 degrees of view by the original 3DGS method from a dataset that contains the 16:40 and 17:27 time intervals images.

The video represents the Sheep-Sculpture rendering at 16:59 from 360 degrees of view by our time-dependent modeling method from a dataset that contains the 16:40 and 17:27 time intervals images.