6 papers
UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation
Yohann Perron, Guillaume Astruc, Nicolas Gonthier +2
Vision Transformers (ViT) dominate computer vision. However, their reliance on rigid patch projectors hinders transfer to Earth Observation (EO), where input modalities, scales, an…
UNIGEOCLIP: Unified Geospatial Contrastive Learning
Guillaume Astruc, Eduard Trulls, Jan Hosang +2
The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity f…
AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
Guillaume Astruc, Nicolas Gonthier, Clement Mallet +1
Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurati…
Loose Social-Interaction Recognition in Real-world Therapy Scenarios
Abid Ali, Rui Dai, Ashish Marisetty +5
The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying-object, etc. However, with the advancement in deep learning models, ther…
OmniSat: Self-Supervised Modality Fusion for Earth Observation
Guillaume Astruc, Nicolas Gonthier, Clement Mallet +1
The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current mul…
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis +10
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of…