SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
Pytorch: An imperative style, high-performance deep learning library
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SEPatch3D accelerates ViT-based 3D object detectors up to 57% faster than StreamPETR via dynamic patch sizing and cross-granularity enhancement while keeping comparable accuracy on nuScenes and Argoverse 2.
An unsupervised SfT approach using image observations and mesh inextensibility constraints reconstructs deforming 3D shapes 400x faster than prior unsupervised methods while handling severe occlusions better.
citing papers explorer
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SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
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Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors
SEPatch3D accelerates ViT-based 3D object detectors up to 57% faster than StreamPETR via dynamic patch sizing and cross-granularity enhancement while keeping comparable accuracy on nuScenes and Argoverse 2.
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Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints
An unsupervised SfT approach using image observations and mesh inextensibility constraints reconstructs deforming 3D shapes 400x faster than prior unsupervised methods while handling severe occlusions better.