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Found 10 papers, 4 papers with code
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity.
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task.
Deep Confidence Guided Distance for 3D Partial Shape Registration
We present a novel non-iterative learnable method for partial-to-partial 3D shape registration.
Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation maps
Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation.
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction.
Ranked #4 on
3D Dense Shape Correspondence
on SHREC'19
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length.
Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration
We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus which is robust to noise as well as the full spectrum of the rotation group.
Unsupervised Scale-Invariant Multispectral Shape Matching
Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets.
Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment
We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities.
Cyclic Functional Mapping: Self-supervised correspondence between non-isometric deformable shapes
We present the first utterly self-supervised network for dense correspondence mapping between non-isometric shapes.
