Naga Venkata Sai Raviteja Chappa
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Formerly Arkansas Graduate Research Assistant, University of Arkansas through 2025.
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Naga Venkata Sai Raviteja Chappa's research focuses on developing and applying advanced deep learning techniques for video analysis, particularly in the areas of group activity recognition and image deblurring. His work has explored self-supervised, spatiotemporal, and attention-based transformer approaches to accurately identify and understand actions within video sequences. Chappa has also investigated multi-modal methods, integrating LiDAR data with visual information for enhanced activity recognition. His research extends to applying these techniques to real-world problems, such as assessing tobacco usage in social media videos. Chappa collaborates with researchers across disciplines, including those at the University of Arkansas for Medical Sciences and within the University of Arkansas at Fayetteville, contributing to a shared publication record.
Metrics
- h-index: 4
- Publications: 19
- Citations: 66
Positions
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Children's Hospital of Philadelphia 2025–presentORCID
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Graduate Research Assistant 2021–2025University of Arkansas at Fayetteville Computer Science and Computer Engineering ORCID
Selected Publications
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LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition (2025)
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SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition (2025)
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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention (2025)
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Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
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Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
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Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
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FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024)
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FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024)
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React: recognize every action everywhere all at once (2024)
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HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos (2024)
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Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos (2024)
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Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods (2024)
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SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition (2023)
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EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
Collaboration Network
Top Collaborators
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- React: recognize every action everywhere all at once
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
Showing 5 of 14 shared publications
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- React: recognize every action everywhere all at once
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
- Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos
Showing 5 of 11 shared publications
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- React: recognize every action everywhere all at once
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
- Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos
- Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods
- Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media
- Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media
- Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media
- Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media
- DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos
- Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods
- FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis
- FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
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