Dynamic Visual Sub-Embeddings for Image-Text Retrieval
Learns several dynamic visual sub-embeddings per image to capture varied semantics and adds a fast re-ranking stage to improve image-text retrieval.
Citation
Wei W., Gui Z., Wu C., Zhao A., Peng D., Wu H. (2025). Dynamic visual semantic sub-embeddings and fast re-ranking for image-text retrieval. IEEE Transactions on Multimedia, 27, 3781–3796. doi:10.1109/TMM.2025.3535373
Cover figure: Overview of the model. From Wei et al. (2025), IEEE Transactions on Multimedia.
Authors
PhD Student in Geospatial AI
I am a PhD student at the Chair of Cartography and Visual Analytics
at the Technical University of Munich, supervised by Prof. Liqiu Meng and
mentored by Prof. Yu Feng. I came to AI from maps. I studied geodesy,
cartography and GIS at Wuhan University, where I first used language
models to make sense of geospatial code, and I now spend most of my time
on the AI side of GeoAI. Besides research, I supervise Master’s students
and maintain open learning resources on LLM agents and web scraping.