Naga Venkata Sai Raviteja Chappa Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Graduate Research Assistant

University of Arkansas at Fayetteville

grad_student

3 h-index 18 pubs 51 cited

Research Areas

Advanced Neural Network Applications Computer Vision and Pattern Recognition Machine Learning for Activity Recognition Deep Learning for Video Analysis Multi-modal Learning Self-Supervised Learning Image Deblurring Techniques

Links

ORCID

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OverviewAI-generated summary

Naga Venkata Sai Raviteja Chappa's research focuses on advancing computer vision and deep learning techniques for activity recognition in videos and other data modalities. His work includes developing self-supervised and transformer-based approaches for identifying group activities, as seen in publications like "SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition" and "SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition." He also investigates multi-modal learning, incorporating data such as LiDAR for improved recognition, as demonstrated in "LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition." Chappa's research extends to image enhancement, with work on domain adaptation for image deblurring, and also includes applications in health-related assessments, such as using deep learning for tobacco usage assessment in social media videos. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Page D. Dobbs and Pha Nguyen.

Metrics

  • h-index: 3
  • Publications: 18
  • Citations: 51

Selected Publications

  • LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition (2025) DOI
  • SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition (2025) DOI
  • DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention (2025) DOI
  • Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024) DOI
  • Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (2024) DOI
  • FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024) DOI
  • FLAASH: Flow-Attention Adaptive Semantic Hierarchical Fusion for Multi-Modal Tobacco Content Analysis (2024) DOI
  • React: recognize every action everywhere all at once (2024) DOI
  • HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos (2024) DOI
  • Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos (2024) DOI
  • Assessing TikTok Videos Content of Tobacco Usage by Leveraging Deep Learning Methods (2024) DOI
  • SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition (2023) DOI
  • EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022) DOI

Collaborators

Researchers in the database who share publications

Page D. Dobbs University of Arkansas at Fayetteville 5 shared publications Pha Nguyen University of Arkansas at Fayetteville 4 shared publications Khoa Luu University of Arkansas at Fayetteville 3 shared publications Charlotte McCormick University of Arkansas at Fayetteville 3 shared publications Han‐Seok Seo University of Arkansas at Fayetteville 2 shared publications Alexander Nelson University of Arkansas at Fayetteville 2 shared publications Pierce Helton University of Arkansas at Fayetteville 1 shared publication Chase Rainwater University of Arkansas at Fayetteville 1 shared publication

Similar Researchers

Based on overlapping research topics

Pha Nguyen University of Arkansas at Fayetteville
Advanced Neural Network Applications Computer Vision and Pattern Recognition Machine Learning for Activity Recognition
Good match 3 shared topics