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Low-rank adaptation (LoRA) often underperforms full fine-tuning. We investigate this performance gap through a tangent-space lens: factorized updates are confined to the tangent space of the current low-rank factors, leaving gradient components outside this space unexploited. An analysis of ten representative LoRA vari...
Existing head pose estimation (HPE) methods focus on utilizing the information of one single image and ignore cross-viewpoint pose consistency. To remedy this, we propose a dual-branch self-correcting pose consistency network (DSPC-Net) for HPE. The idea is to construct flipped image pairs as intrinsic pose constraints...
In real-world scenarios, point clouds can take on diverse poses and suffer from incompleteness. Point cloud completion benefits from rotation-invariant feature representations, which remain largely unaddressed in current study. To this end, we propose a Self-Contour Invariant Representation-based Completion Network (SC...
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