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Found 8 papers, 3 papers with code
Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility
In this paper, we detail the methods we used to determine the idiomaticity and plausibility of candidate words or phrases into an instructional text as part of the SemEval Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts.
CAT: Contrastive Adapter Training for Personalized Image Generation
Finally, we mention the possibility of CAT in the aspects of multi-concept adapter and optimization.
Self-Supervised Pre-Training for Precipitation Post-Processor
Obtaining a sufficient forecast lead time for local precipitation is essential in preventing hazardous weather events.
PeRFception: Perception using Radiance Fields
The recent progress in implicit 3D representation, i. e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner.
Learning to Register Unbalanced Point Pairs
The proposed method first predicts subregions within target point cloud that are likely to be overlapped with query.
Putting 3D Spatially Sparse Networks on a Diet
3D neural networks have become prevalent for many 3D vision tasks including object detection, segmentation, registration, and various perception tasks for 3D inputs.
Deep Hough Voting for Robust Global Registration
We then construct a set of triplets of correspondences to cast votes on the 6D Hough space, representing the transformation parameters in sparse tensors.
High-dimensional Convolutional Networks for Geometric Pattern Recognition
Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces.
