arXiv AI August 12, 2026 By Sebastian Regalado, Varshanth R. Rao, Ruowei Jiang, Parham Aarabi, Igor Gilitschenski

Towards Unified Dynamic Face Landmark Detection

arXiv:2608. 10346v1 Announce Type: cross Abstract: Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations: (1) different network parameters need to be trained independently for each ``$N$-point'' benchmark dataset, and (2) a model trained on an ``$N$-point'' dataset reliably outputs only the $N$ landmarks.

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benchmarks

arXiv Computer Vision

Sep 10

FreqFLD: Towards All-in-One Facial Landmark Detection via Frequency Modulation

arXiv:2609.10278v1 Announce Type: new Abstract: Recent progress in deep learning has significantly advanced facial landmark detection. However, most existing methods process features in a spatial-dom...

By Shun Ren, Kaijie Jin, Shengkai Hu, Beihang Song, Hang Sun, Wenwen Min, Youfa Liu, Jun Wan

arXiv Machine Learning

Aug 11

Compatibility of Face Embeddings Across Deep Neural Networks

arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.

By Fizza Rubab, Yiying Tong, Arun Ross

rag multimodal safety

arXiv Computer Vision

Sep 2

Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models

arXiv:2609.00411v1 Announce Type: new Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These...

By Fizza Rubab, Yiying Tong, Arun Ross

rag diffusion safety

arXiv AI

Jul 7

PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition

arXiv:2607. 03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems.

By Dana A Abdullah

llms multimodal

arXiv AI

Sep 15

A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification

arXiv:2609.14419v1 Announce Type: cross Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chan...

By Leon Fernando, C Dombawala, P. Hettigoda, Vanodhya G. Warnasooriya, Ishara Neranjana, Rashmika Nawaratne

llms rag multimodal benchmarks

arXiv AI

Sep 25

WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model

WildHSR introduces a lightweight adaptation of 3D foundation models to jointly recover metric cameras, scene geometry, and persistent person identities from monocular video. By generating pseudo‑scale labels from curated web footage and fine‑tuning a Scale Readout, the method predicts metric scale directly from foundation‑model tokens. It also exploits intermediate query‑key features to associate per‑frame bodies, enabling feed‑forward reconstruction that outperforms state‑of‑the‑art optimization‑based methods on several benchmarks while running at 10.1 fps.

By Jerrin Bright, John Zelek

fine-tuning

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Related stories

FreqFLD: Towards All-in-One Facial Landmark Detection via Frequency Modulation

Compatibility of Face Embeddings Across Deep Neural Networks

Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models

PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition

A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification

WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model