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Articles by Page Daniel
DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention
[Submitted on 20 Jan 2025] Title:DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention View a PDF of the paper titled DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention, by Naga VS Raviteja Chappa and 4 other authors View PDF HTML (experimental) Abstract:While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of social media.
Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media
[Submitted on 12 Nov 2024] Title:Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media View a PDF of the paper titled Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media, by Naga VS Raviteja Chappa and 3 other authors View PDF HTML (experimental) Abstract:The Public Health Advocacy Dataset (PHAD) is a comprehensive collection of 5,730 videos related to tobacco products sourced from social media platforms like TikTok and YouTube.
FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis
[Submitted on 25 Oct 2024] Title:FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis View a PDF of the paper titled FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis, by Naga VS Raviteja Chappa and 2 other authors View PDF HTML (experimental) Abstract:The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention.
REACT: Recognize Every Action Everywhere All At Once
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
[Submitted on 27 Apr 2023 ( v1 ), last revised 18 Nov 2024 (this version, v4)] Title:SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition View a PDF of the paper titled SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition, by Naga VS Raviteja Chappa and 6 other authors View PDF HTML (experimental) Abstract:This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers...
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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