Duy Anh Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Duy Anh's research investigates advancements in artificial intelligence and machine learning, with a focus on object tracking and optimization algorithms. His recent publications explore enhanced Kalman filters for multiple object tracking, incorporating adaptive appearance and motion cues, as well as textual prompts for improved tracking accuracy. He also studies the convergence rates of gradient descent-ascent dynamics under delays in non-convex min-max optimization and asynchronous policy iteration for zero-sum Markov games. Additionally, Anh has contributed to research on deep learning in spiking neural networks (SNNs) through local learning and surrogate-derivative transfer. His work also touches upon challenges in English as a foreign language learning. Anh has co-authored publications with Kim Hoang Tran at the University of Arkansas at Fayetteville.
Metrics
- h-index: 2
- Publications: 9
- Citations: 27
Selected Publications
-
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking (2024)
-
TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT (2024)
Collaboration Network
Top Collaborators
- Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings
- Convergence Rates of Gradient Descent-Ascent Dynamics Under Delays in Solving Nonconvex Min-Max Optimization
- DALTON - Deep Local Learning in SNNs via Local Weights and Surrogate-Derivative Transfer
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
- Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings
- Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings
- An investigation into difficulties in listening and speaking English as a foreign language
- DALTON - Deep Local Learning in SNNs via Local Weights and Surrogate-Derivative Transfer
- DALTON - Deep Local Learning in SNNs via Local Weights and Surrogate-Derivative Transfer
- TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
- TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
Similar Researchers
Based on overlapping research topics