AugMMRev: An LLM-Augmented Multimodal Ranking Model for Personalized Image Material Retrieval

Consumer electronics devices—including smartphones and smart cameras—generate massive volumes of image data. Image retrieval serves as a critical enabling technology for diverse image-centric applications in consumer electronic applications (like AI-powered photo retrieval in smartphone, efficient media asset and creative template retrieval in smart camera, streaming recommendation systems for smart TVs). Nevertheless, text-query-based image retrieval encounters unique challenges within consumer electronics environments. First, in consumer electronics applications, text-to-image retrieving queries are typically concise, frequently leading to ambiguity in intent. Second,numerous images lack textual descriptions…

• GN
  Gyeongnam National University of Science and Technology

  Award: 2024-ksj3
• NK
  National Key Research and Development Program of China

  Awards: Grant No. 2023YFF0905502, 2023YFF0905502
• FR
  Fundamental Research Funds for the Provincial Universities of Zhejiang

  Award: GK249909299001-017