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Ho Chi Minh City University of Technology

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Research library51linked papers
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Selected work

Representative Papers

A Revisit to Point Estimation Through the Empirical Bayes Method: The Case of Binomial Distribution with Beta Prior and Extension to Poisson Distribution

Aug 13, 2026

This study reevaluates the efficacy of empirical Bayes methods for parameter estimation in binomial (with Beta priors) and Poisson models. Through theoretical analysis and extensive numerical experiments, it specifically investigates Type-II maximum likelihood (ML-II) under general two-parameter Beta priors and extends the examination to the Gamma–Poisson setting. The findings reveal that the ML-II procedure fails under a general two-parameter Beta prior; even when restricted to a symmetric one-parameter Beta prior, the resulting estimator does not substantially outperform the maximum likelihood estimator under quadratic loss. These results challenge the commonly presumed superiority of empirical Bayes approaches in point estimation and provide a critical counterexample grounded in rigorous empirical evidence.

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FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search

Aug 10, 2026

This work addresses the challenge of fine-grained matching in text-based pedestrian anomaly retrieval under synthetic-to-real (Sim2Real) scenarios. To this end, the authors propose an anchor-constrained coarse-to-fine retrieval framework that leverages multi-facet semantic decomposition and calibrated fusion. The approach integrates a heterogeneous vision-language retriever, a Qwen3-based reranker, and an anomaly-aware cloze-style verification module, complemented by an uncertainty-gated consensus mechanism operating over a small candidate pool to enable efficient fine-grained semantic reasoning. Innovatively, semantic facets serve as anchor constraints to jointly optimize recall and computational efficiency. Evaluated on the PAB benchmark, the method achieves 95.41% mAP@10, 94.44% R@1, and 99.09% R@5, significantly outperforming existing single-backbone models.

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Recent publications

Latest Papers

A Revisit to Point Estimation Through the Empirical Bayes Method: The Case of Binomial Distribution with Beta Prior and Extension to Poisson Distribution

Aug 13, 2026

This study reevaluates the efficacy of empirical Bayes methods for parameter estimation in binomial (with Beta priors) and Poisson models. Through theoretical analysis and extensive numerical experiments, it specifically investigates Type-II maximum likelihood (ML-II) under general two-parameter Beta priors and extends the examination to the Gamma–Poisson setting. The findings reveal that the ML-II procedure fails under a general two-parameter Beta prior; even when restricted to a symmetric one-parameter Beta prior, the resulting estimator does not substantially outperform the maximum likelihood estimator under quadratic loss. These results challenge the commonly presumed superiority of empirical Bayes approaches in point estimation and provide a critical counterexample grounded in rigorous empirical evidence.

0 citationsRead paper

FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search

Aug 10, 2026

This work addresses the challenge of fine-grained matching in text-based pedestrian anomaly retrieval under synthetic-to-real (Sim2Real) scenarios. To this end, the authors propose an anchor-constrained coarse-to-fine retrieval framework that leverages multi-facet semantic decomposition and calibrated fusion. The approach integrates a heterogeneous vision-language retriever, a Qwen3-based reranker, and an anomaly-aware cloze-style verification module, complemented by an uncertainty-gated consensus mechanism operating over a small candidate pool to enable efficient fine-grained semantic reasoning. Innovatively, semantic facets serve as anchor constraints to jointly optimize recall and computational efficiency. Evaluated on the PAB benchmark, the method achieves 95.41% mAP@10, 94.44% R@1, and 99.09% R@5, significantly outperforming existing single-backbone models.

0 citationsRead paper