WorldComp2D explicitly structures latent space geometry by object identity and spatial proximity via a proximity-dependent encoder and localizer, cutting parameters up to 4X and FLOPs 2.2X versus state-of-the-art lightweight models on facial landmark localization while staying real-time on CPU.
IEEE transactions on pattern analysis and machine intelligence , volume=
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Authors generated and released 3,000 unlabeled field and 4,000 labeled synthetic seismic datasets for global shelf-edge clinothems to enable deep learning for automated seismic stratigraphic interpretation.
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WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
WorldComp2D explicitly structures latent space geometry by object identity and spatial proximity via a proximity-dependent encoder and localizer, cutting parameters up to 4X and FLOPs 2.2X versus state-of-the-art lightweight models on facial landmark localization while staying real-time on CPU.
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Massive-scale unlabeled field and labeled synthetic seismic datasets of global shelf-edge clinothems
Authors generated and released 3,000 unlabeled field and 4,000 labeled synthetic seismic datasets for global shelf-edge clinothems to enable deep learning for automated seismic stratigraphic interpretation.
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