Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

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Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

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The Arctic is one of the most rapidly warming regions of the globe. Low-level clouds and fog modify the energy transfer from and to space and play a key role in the observed strong Arctic surface warming, a phenomenon commonly termed "Arctic amplification" ...

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Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

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Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

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K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple data t ...

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

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Data-driven approaches for non-invasive cuffless blood pressure monitoring

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Evaluating the effect of sparse convolutions on point cloud compression

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Signal strength and integrated intensity in confocal and image scanning microscopy

Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds

Performance of Social Machine Learning under Limited Data

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Combination of dielectrophoretic and hydrodynamic traps for the controlled contact between cells or beads and application to adhesion assays

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

The role of secondary ice production in mixed-phase clouds

Improving K-means Clustering Using Speculation

Improving Object Detection under Domain Shifts

XTab: Cross-table Pretraining for Tabular Transformers