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Multi-Modal Joint Embedding Space Learning for Cross-Modalit Retrieval

  • Matthias Dorfer (Vortragende*r)

Aktivität: Vortrag oder PräsentationEingeladener VortragScience-to-science

Beschreibung

Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query. The state-of-the-art approach to cross-modality retrieval relies on learning a joint embedding space of the two modalities, where items from either modality are retrieved using nearest-neighbor search. In my talk I will review two different learning paradigms -- Deep Canonical Correlation Analysis and Pairwise Ranking Losses — which both yield embedding spaces exhibiting properties beneficial for retrieval. I will present potential application scenarios as well as experimental retrieval results on two different modality pairs, namely text and images as well as audio and sheet-music.
Zeitraum16 Okt. 2018
Ereignistitelunbekannt/unknown
VeranstaltungstypSonstiges
OrtÖsterreichAuf Karte anzeigen

Wissenschaftszweige

  • 202002 Audiovisuelle Medien
  • 102 Informatik
  • 102001 Artificial Intelligence
  • 102015 Informationssysteme
  • 102003 Bildverarbeitung

JKU-Schwerpunkte

  • Computation in Informatics and Mathematics
  • TNF Allgemein