Christel Marie Tartini-Chappuis

Chattez avec Graph Search

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Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

Image-based retrieval in large Earth observation archives is difficult, because one needs to navigate across thousands of candidate matches only with the proposition image as a guide. By using text as a query language, the retrieval system gains in usabili ...

Remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in ...

Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and c ...

Introduced to enable a wider use of Earth Observation images using natural language, Remote Sensing Visual Question Answering (RSVQA) remains a challenging task, in particular for questions related to counting. To address this specific challenge, we propos ...

Christel Marie Tartini-Chappuis

Chattez avec Graph Search

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Knowledge-Aware Cross-Modal Text-Image Retrieval for Remote Sensing Images

Prompt–RSVQA: Prompting visual context to a language model for Remote Sensing Visual Question Answering

Language Transformers for Remote Sensing Visual Question Answering

Multi-task prompt-RSVQA to explicitly count objects on aerial images

How to find a good image- text embedding for remote sensing visual question answering?