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Forsaking your own: unveiling the delayed recognition of Garfield's work on the "delayed recognition" phenomenon
arXiv:2512.16943v4 Announce Type: replace Abstract: Delayed recognition (DR) implies that the full scholarly potential of certain scientific papers is recognized belatedly many years after their publication. Such papers are initially barely cited (sleep), and then suddenly, sometime in the future, their citation numbers burst (are awakened). After van Raan (2004a) called them "Sleeping Beauties" the DR phenomenon has drawn considerable attention.
Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning
arXiv:2606.09353v1 Announce Type: new Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms. Present recognition techniques mostly use physical devices, e.g., microchips, often impractical and difficult to apply. These could be replaced by remote recognition via the animal's face; if accurate enough, it provides several advantages: it is non-invasive, can...
Wired found code for an unreleased facial recognition feature in Meta's AI app
Wired found code for an unreleased facial recognition feature in Meta's AI app Meta was previously reported to be exploring facial recognition for its smart glasses. Code for a facial recognition feature that can run on Meta smart glasses is buried in the company's Meta AI app, according to a new report from Wired. While not currently enabled, accessible to customers or part of a formerly announced feature, the code appears to be further evidence that Meta is considering how facial...
ST-ColoNet: Spatio-Temporal Colon Segment Recognition via Hybrid Attention and Edge-Guided Feature Learning
arXiv:2605.28119v3 Announce Type: replace Abstract: Colo-segment recognition in colonoscopy videos is a key requirement for many downstream tasks, but existing automatic recognition methods only use colonoscopy images without fully exploiting the use of temporal information, leading to poor performance. Additionally, relevant public video-based datasets are in scarcity. To tackle this problem, we curate and release a labeled dataset specifically for the task of colo-segment recognition.
ST-ColoNet: Spatio-Temporal Colon Segment Recognition via Hybrid Attention and Edge-Guided Feature Learning
Announce Type: replace Abstract: Colo-segment recognition in colonoscopy videos is a key requirement for many downstream tasks, but existing automatic recognition methods only use colonoscopy images without fully exploiting the use of temporal information, leading to poor performance. Additionally, relevant public video-based datasets are in scarcity. To tackle this problem, we curate and release a labeled dataset specifically for the task of colo-segment recognition.
M2S-AVSR: Modality-aware Multi-view Self-supervised Representation for Robust Audio-Visual Speech Recognition
arXiv:2606.05763v1 Announce Type: cross Abstract: Audio-Visual Speech Recognition (AVSR) enhances speech recognition robustness by leveraging visual cues, while real-world scenarios remain challenging due to viewpoint variation, audio distortion, and visual occlusion, which degrade modality quality and increase audio-visual asynchrony. In this paper, we propose a novel Modality-aware Multi-view Self-supervised representation framework for robust Audio-Visual Speech Recognition (M2S-AVSR)....
M2S-AVSR: Modality-aware Multi-view Self-supervised Representation for Robust Audio-Visual Speech Recognition
arXiv:2606.05763v2 Announce Type: replace-cross Abstract: Audio-Visual Speech Recognition (AVSR) enhances speech recognition robustness by leveraging visual cues, while real-world scenarios remain challenging due to viewpoint variation, audio distortion, and visual occlusion, which degrade modality quality and increase audio-visual asynchrony. In this paper, we propose a novel Modality-aware Multi-view Self-supervised representation framework for robust Audio-Visual Speech Recognition...
Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis
arXiv:2511.10254v2 Announce Type: replace Abstract: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrates three subtasks: emotion recognition, facial Action Unit (AU) recognition, and AU-based emotion reasoning to model affective states jointly. While recent approaches leverage Vision-Language Models (VLMs) and achieve promising results, they face two critical limitations: (1) hallucinated...
Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation
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Meta's ships facial recognition on smart glasses
Meta's smart glasses companion app ships a complete, dormant face-recognition pipeline on a stock account. Stella is the companion app for Meta's smart glasses. Inspecting version 273.0.0.21 of the Android build (com.facebook.stella ), I found the entire computational and storage stack for on-device facial recognition: three face models, a local database schema, a cosine-similarity vector index dimensioned to match the models, a write path that stages biometric records to disk, a fully wired...