We propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long- ...
Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user's feedback history. Most researchers strive to develop new ...
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In this study, we propose a reinforcement learning framework, namely RML, that learns a relevance feedback function as a stochas- tic policy network by ...
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Jul 25, 2020 · We present RML, the first known general reinforcement learning framework for relevance feedback that directly optimizes any desired retrieval metric.
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Peng-Yeng Yin, Bir Bhanu , Kuang-Cheng Chang, Anlei Dong: Reinforcement Learning for Combining Relevance Feedback Techniques. ICCV 2003: 510-515.
Dec 6, 2023 · The training process incorporates three stages: supervised fine-tuning, relevance reward model training, and reinforced learning-to-rank from.
In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are ...
RML is presented, the first known general reinforcement learning framework for relevance feedback that directly optimizes any desired retrieval metric, ...
Missing: Combining | Show results with:Combining
In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval ...
Reinforcement Learning from Experience Feedback - IMF eLibrary
www.elibrary.imf.org › article-A001-en
Jun 7, 2024 · RLHF is a popular technique for fine-tuning large language models using reinforcement learning with reward signals provided directly by humans.