SARVLM is the first vision-language foundation model for SAR, trained via domain transfer on a 1M image-text dataset and outperforming prior models on 13 benchmarks for retrieval, recognition, detection, and captioning.
Cider: Consensus-based image description evalua- tion
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A new framework combines self-attention on the Oblique manifold with bidirectional geodesic cross-attention on the Lorentz hyperboloid to improve both localization accuracy and descriptive coherence in 3D dense captioning.
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SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery
SARVLM is the first vision-language foundation model for SAR, trained via domain transfer on a 1M image-text dataset and outperforming prior models on 13 benchmarks for retrieval, recognition, detection, and captioning.
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Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding
A new framework combines self-attention on the Oblique manifold with bidirectional geodesic cross-attention on the Lorentz hyperboloid to improve both localization accuracy and descriptive coherence in 3D dense captioning.