Vision-language pre-training methods, e.g., CLIP, demonstrate an impressive zero-shot performance on visual categorizations with the class proxy from the text embedding of the class name.

The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples.

Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build afully sparse convolutional RoI pooling module todirectly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific featuresofeach3Dproposal.

Neural Information Processing Systems http://nips.cc/

Dayan's SR [3] is well-suited for transfer learning in settings with fixed dynamics, as the decomposition ofthevaluefunction intorepresentations ofexpected outcomes (future stateoccupancies) andcorresponding rewards allowsustoquickly recompute values under newrewardsettings.

Thestandard supervised learning paradigm workseffectivelywhentraining data shares the same distribution as the upcoming testing samples.