EgoForce recovers absolute camera-space 3D hand pose from monocular egocentric images using forearm guidance, a unified arm-hand transformer, and a closed-form ray-space solver that handles fisheye, perspective, and wide-FOV cameras.
Advances in Neural Information Processing Systems (NeurIPS) , year=
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UNVERDICTED 3representative citing papers
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
Varying evaluation metrics and corruption methods in activation patching produces different localization and circuit discovery outcomes in language models, leading to recommendations for preferred practices.
citing papers explorer
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EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera
EgoForce recovers absolute camera-space 3D hand pose from monocular egocentric images using forearm guidance, a unified arm-hand transformer, and a closed-form ray-space solver that handles fisheye, perspective, and wide-FOV cameras.
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Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
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Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
Varying evaluation metrics and corruption methods in activation patching produces different localization and circuit discovery outcomes in language models, leading to recommendations for preferred practices.