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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.

This work investigates Source-Free Domain Adaptation (SFDA), where a model adapts to a target domain without access to source data. A new augmentation technique, Shuffle PatchMix (SPM), and a novel reweighting strategy are introduced to enhance performance. SPM shuffles and blends image patches to generate diverse and challenging augmentations, while the reweighting strategy prioritizes reliable pseudo-labels to mitigate label noise. These techniques are particularly effective on smaller datasets like PACS, where overfitting and pseudo-label noise pose greater risks.

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.

Abstract

In nighttime conditions, high noise levels and bright Illumination sources degrade image quality, making low-light image enhancement challenging. Thermal images provide complementary information, offering richer textures and structural details. We propose RT-X Net, a cross-attention network that fuses RGB and thermal images for nighttime image enhancement. We leverage self-attention networks for feature extraction and a cross-attention mechanism for fusion to effectively integrate information from both modalities.

SUPPLEMENTARY MATERIALS FOR EVENT DENOISING BASED ON ITERATIVE
TREE-STRUCTURED INFORMATION AGGREGATIO

Supplement for the research paper CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION

This document contains supplementary material for the ICIP PAPER.

This document contains supplementary material for the ICIP PAPER.