arXiv Computer Vision
Two Global Crops Suffice: Locating Semantic Emergence in DINO-Style Self-Supervised Learning
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A Mixed Diet Makes DINO An Omnivorous Vision Encoder
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DINOcular: Self-Supervised Visuospatial Representations
DINOcular is a self‑supervised framework that learns joint visuospatial representations from RGB‑D data. It fuses depth‑derived geometric priors with a visual backbone using inter‑patch and intra‑patch techniques, allowing the model to encode both appearance and spatial structure efficiently. The resulting representation improves 3D awareness on multiple geometry benchmarks while staying competitive on standard RGB‑D semantic segmentation tasks.
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Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
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Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation
Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity.