Match tier
Confirmed
Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
N
Naga Venkata Sai Raviteja Chappa
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Graduate Research Assistant
Graduate Student Researcher
4 h-index
20 pubs
64 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Naga Venkata Sai Raviteja Chappa, a graduate research assistant at the University of Arkansas at Fayetteville, focuses on developing advanced deep learning techniques for activity recognition in videos and images. His work explores self-supervised and attention-based methods, often incorporating spatiotemporal transformers and hierarchical attention mechanisms. Chappa has investigated approaches for group activity recognition, such as SPARTAN and SoGAR, and has also applied deep learning to specific challenges like image deblurring with EQAdap. His research extends to real-world applications, including the assessment of tobacco usage in TikTok videos. Chappa has a publication record that includes work on multi-modal group activity recognition utilizing LiDAR data (LiGAR) and has collaborated with researchers including Page D. Dobbs, Pha Nguyen, Khoa Luu, and Charlotte McCormick.
Metrics
- h-index: 4
- Publications: 20
- Citations: 64
Selected Publications
-
LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition (2025)
-
SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition (2025)
-
DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention (2025)
-
Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
-
Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
-
Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024)
-
FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024)
-
FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024)
-
React: recognize every action everywhere all at once (2024)
-
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos (2024)
-
Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos (2024)
-
Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods (2024)
-
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition (2023)
-
EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
Collaboration Network
Top Collaborators
Khoa Luu
University of Arkansas at Fayetteville
14 shared publications
In database
- 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
Page D. Dobbs
University at Albany, State University of New York
11 shared publications
In database
- 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
Pha Nguyen
University of Arkansas at Fayetteville
4 shared publications
In database
- 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
Susana Rodriguez Gongora
University of Arkansas at Fayetteville
4 shared publications
In database
- 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
Charlotte McCormick
3 shared publications
In database
- 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
Alexander Nelson
University of Arkansas at Fayetteville
2 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
Han‐Seok Seo
University of Arkansas at Fayetteville
2 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
Charlotte Mccormick
2 shared publications
- Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos
- Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods
Bhiksha Raj
Carnegie Mellon University (US)
2 shared publications
- 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
Ibsa Jalata
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Thanh-Dat Truong
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Pierce Helton
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Chase Rainwater
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Xin Li
West Virginia University (US)
1 shared publication
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
Ngan Le
University of Arkansas at Fayetteville
1 shared publication
In database
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
Similar Researchers
Based on overlapping research topics
Taisei Hanyu
University of Arkansas at Fayetteville
Computer Vision and Image Analysis
Machine Learning Applications
Advanced Neural Network Applications
Israel Cuevas
University of Arkansas at Fayetteville
Machine Learning Applications
Advanced Neural Network Applications
Media Studies and Communication
Shubhalaxmi Kher
Arkansas State University
Machine Learning Applications
Advanced Neural Network Applications
Computer Graphics and Visualization Techniques
Qixin Wang
University of Arkansas at Fayetteville
Machine Learning Applications
Advanced Neural Network Applications
Computer Graphics and Visualization Techniques
Khoa Vo
University of Arkansas at Fayetteville
Machine Learning Applications
Advanced Neural Network Applications
Computer Graphics and Visualization Techniques
Gabriel J. Ferrer
Hendrix College
Machine Learning Applications
Computer Graphics and Visualization Techniques
Media Studies and Communication