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Found 15 papers, 3 papers with code
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.
Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images
Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches.
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process.
The onset of molecule-spanning dynamics in a multi-domain protein
Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes.
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style.
Learning the Arrow of Time for Problems in Reinforcement Learning
We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.
Instance Separation Emerges from Inpainting
Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly.
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.
Learning the Arrow of Time
We humans seem to have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation
We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments; or equivalently, the task of detecting closed contours in an image.
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing.
Learned Watershed: End-to-End Learning of Seeded Segmentation
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm.
Tracking Objects with Higher Order Interactions using Delayed Column Generation
We study the problem of multi-target tracking and data association in video.
