Model sequences through their segmentations using dynamic programming.
problem Modeling sequences with unknown segmentations.
method Probabilistic model based on segmentations, using existing tools for segments, summing over all valid segmentations, dynamic programming for efficient computation.
result Demonstrated on text and speech recognition, discovering meaningful segments.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
A new method models uncertainty in medical image segmentation.
problem Inherent ambiguity in anatomical structure and pathology segmentation.
method Hierarchical probabilistic model with variational autoencoder.
result Generates more realistic and diverse segmentation samples.
New CRF model segments music into chords with rich features.
problem Harmonic analysis of music segments.
method Semi-Markov Conditional Random Field (semi-CRF) with segment-level features.
result Semi-CRF model performs better than previous approaches.
Generative model learns distribution over plausible segmentations.
problem Learning from ambiguous images in clinical applications.
method Probabilistic U-Net combining U-Net and conditional variational autoencoder.
result Significantly better at reproducing plausible segmentations than previous methods.
Dynamic segmentation algorithm improves NMT performance by favoring character-level processing.
problem Suboptimal static segmentation choices in NMT systems.
method Adaptive Computation Time algorithm for dynamic segmentation, trainable end-to-end.
result The model prefers character-level processing when given the freedom to navigate different segmentation levels.
Differentiable segmented models for non-stationary data.
problem Estimating change points in non-stationary sequential data.
method Formulated a relaxed variant of segmented models that enables gradient descent for all parameters, including segmentation.
result Successfully learned all tasks with standard gradient descent algorithms.
Bayesian approach labels segment parameters for improved change detection.
problem Improving Bayesian change point detection by leveraging segment parameter patterns.
method Proposes a Bayesian mean-shift change point detection algorithm with a Dirichlet process prior for segment class labels.
result Enhanced performance in synthetic and real-world data.
Novel framework monitors cardiac image segmentation models in real-time.
problem Ensuring continuous high model performance and segmentation results in clinics.
method Formulated as anomaly detection, the framework derives surrogate quality measures for segmentation.
result Demonstrated accurate, fast, and scalable quality control monitoring.
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
A new deep neural network improves short-speech recognition.
problem Improving speaker recognition for short-duration speech segments.
method Proposes UtterIdNet, a novel deep neural network architecture.
result Consistent and stable performance for short segments, especially sub-second durations.
Proposes a new method for deep semantic segmentation learning without needing a pre-trained model.
problem Deep convolutional semantic segmentation learning fails to converge without a pre-trained model.
method Joint cooperative end-to-end learning with improved inception-based architecture and partial attention gating.
result Achieves faster convergence and better accuracy for segmentation tasks.
DeepMRSeg uses deep learning for brain tissue segmentation.
problem Automated segmentation of brain tissues and abnormalities.
method Modified UNet architecture with multi-scale feature extraction.
result DeepMRSeg achieves high accuracy on various brain segmentation tasks.
Adversarial examples mislead deep learning models in medical image segmentation.
problem Vulnerability of deep learning models to adversarial examples in medical image segmentation.
method Proposed Adaptive Segmentation Mask Attack (ASMA) to craft targeted adversarial examples.
result Demonstrated vulnerability of deep learning models to adversarial examples in medical image segmentation.
End-to-end training improves segmental models for speech recognition.
problem Training segmental models is challenging and lacks understanding under different settings and losses.
method Investigated a model class based on recent successful approaches, trained end-to-end with various losses.
result Marginal log loss provides the most consistent strong performance without requiring ground-truth alignments.
Deep learning model uses mixed supervision for brain tumor segmentation.
problem Costly manual tumor segmentation data.
method Extends segmentation networks with an image-level classification branch.
result Significant improvement in segmentation performance.
Agent learns object segmentation through self-supervised interactions.
problem Object segmentation in visual observations.
method Self-supervised learning through interaction, robust set loss.
result Model generalizes to novel objects and backgrounds.
New method estimates image appearance models for segmentation.
problem Estimating appearance models for image segmentation.
method Tensor factorization-based estimator for latent variable models.
result Automatic estimation of image regions and proportions.
This paper improves level generation using VAEs for coherent, logically following segments.
problem Generating coherent levels of non-fixed length and blending levels from different games.
method Sequential segment-based level generation using VAEs with a classifier for logical placement.
result Generated levels are more coherent and capable of blending levels from different games.
Two methods improve 10-K item segmentation using large language models.
problem Challenges in extracting specific items from 10-K reports due to variations in document formats and item presentation.
method Two advanced item segmentation methods: GPT4ItemSeg and BERT4ItemSeg.
result BERT4ItemSeg achieves a macro-F1 of 0.9825, surpassing other methods.
Proposes guided networks for fast, accurate few-shot segmentation.
problem Few supervision limits learning-based segmentation methods.
method Extracts latent task representation from supervision, optimizes end-to-end.
result First results for segmentation from one pixel per concept, real-time interactive video segmentation.
Novel framework for medical image segmentation using deep learning.
problem Class imbalance and domain adaptation in medical image segmentation.
method Biophysics-based domain adaptation and automatic segmentation of white, gray, and cerebrospinal fluid.
result Improved segmentation performance, especially with the biophysics-based domain adaptation.
This paper presents a new probabilistic generative model for image segmentation, i.e. the task of partitioning an image into homogeneous regions. Our model is grounded on a mid-level image representation, called a region tree, in which regions are recursively split into subregions until superpixels are reached. Given t…
Paper improves robustness of semantic segmentation models.
problem Improving robustness of semantic segmentation models for safer interactions.
method Analyzes and corrects softmax function mappings to improve model robustness.
result Improves robustness with minimal performance impact and code changes.
TPM improves medical image segmentation by separating foreground and background.
problem Few-shot medical image segmentation challenges due to background variability.
method Tied Prototype Model (TPM) focusing on foreground, adapting thresholds, and using class priors.
result TPM leads to improved segmentation accuracy compared to ADNet.
Neural model improves text segmentation accuracy.
problem Manual feature engineering and large memory requirements in text segmentation.
method Attention-based bidirectional LSTM model with CNN sentence embeddings and contextual prediction.
result Improves WinDiff score by ~7% on three benchmark datasets.
Deep learning improves automatic image segmentation.
problem Automatic object localization and boundary delineation in images and medical scans.
method Proposed and evaluated novel dilated dense encoder-decoder architectures for salient object segmentation and lesion localization in medical images.
result Proposed architectures outperform state-of-the-art models in accuracy and efficiency.
New method calibrates confidence for object detection and segmentation models.
problem Intrinsically miscalibrated confidence estimates in object detection and segmentation models.
method Introduces multivariate confidence calibration for object detection and segmentation, extending ECE.
result Improves calibration, positively impacts segmentation quality.
Flow-SSN improves segmentation efficiency and accuracy.
problem Challenges in medical imaging segmentation, especially high-rank pixel-wise covariances.
method Generative segmentation model using discrete-time autoregressive and continuous-time flow variants.
result Flow-SSNs can estimate high-rank pixel-wise covariances efficiently without assuming rank or storing parameters.
CNNs can be trained with clinically available segmentations for OARs in radiotherapy.
problem Lack of dedicated training volumes for CNNs in radiotherapy.
method Used clinically available segmentations from PACS, applied multi-label segmentation, empirically assessed training set size.
result Clinically available segmentations can be used to train an accurate OAR segmentation model.
A new model improves medical image segmentation uncertainty.
problem Uncertainty in medical image segmentation.
method Conditional Normalizing Flow (cFlow) for improved segmentation uncertainty.
result Improved quality and diversity of segmentation samples.
Paper presents a brain tumor segmentation method using NGMM and 3D FVF.
problem Automatic brain tumor segmentation from MRIs is challenging.
method Normalized Gaussian Bayesian classifier and 3D Fluid Vector Flow algorithm.
result The method successfully segments brain tumors from MRI images.
FAST optimizes additive segmentation for faster, more interpretable models.
problem Efficiently segmenting and interpreting complex datasets.
method Optimization framework for fast piecewise constant shape functions.
result 2 orders of magnitude faster than state-of-the-art methods.
A new model reduces noise and speeds up subspace segmentation.
problem Subspace segmentation from noisy data.
method Group norm regularized factorization model (GNRFM) with AALM algorithm.
result The method is faster and more robust to noise.
Deep learning improves microscale object segmentation.
problem Accurate estimation of crowded microscale objects.
method Deep learning model for semantic segmentation.
result High mean Intersection Over Union score of 0.91.
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
problem Accurate lung tumor segmentation in mice MRI scans for drug discovery.
method Optimized nnU-Net 3D model for lung tumor segmentation with minimal annotations.
result nnU-Net 3D models outperform 2D models in MRI mice scans, requiring fewer annotations.
Generative model creates realistic scenes from pixel-wise labels.
problem Creating photo-realistic scenes from pixel-wise labels.
method Semantic bottleneck GAN model combining conditional and unconditional generation networks.
result Model outperforms state-of-the-art models in unsupervised image synthesis.
Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.
This review synthesizes uncertainty modeling in probabilistic image segmentation.
problem Relaxed Bayesian assumptions lead to missing uncertainty information in deep models.
method Standardizes theory, notation, and terminology for feature- and parameter-distribution modeling.
result Establishes a common framework for robust decision-making in segmentation tasks.
Improved segmentation model adaptation for new domains.
problem Reduced performance of pre-trained models on new domains.
method Calculated soft-label prototypes and predicted closest to class probabilities.
result Significant performance improvements on synthetic-to-real segmentation.
Paper introduces a semi-supervised method for image segmentation using a small set of labeled images.
problem Costly and time-consuming to create large labeled datasets for semantic segmentation.
method Uses a small set of fully labeled images and a weak set of only bounding box labeled images. Trains a primary model with an ancillary model generating initial labels and a self-correction module improving these labels.
result Models trained with a small fully supervised set perform similarly or better than those trained with a large fully supervised set, requiring significantly less annotation effort.
Preformer improves Transformer for long-term time series forecasting.
problem Transformer's quadratic complexity and lack of context-awareness for long-term forecasting.
method Introduces Multi-Scale Segment-Correlation mechanism for efficient time series segmentation and context-aware attention.
result Preformer outperforms other Transformer-based methods in long-term time series forecasting.
A new method for brain tissue segmentation across medical centers using a smoothness prior.
problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.
New deep learning model generates accurate personalized human head models for electromagnetic dosimetry.
problem Challenges in generating accurate human head models for personalized electromagnetic dosimetry.
method Proposed ForkNet architecture for segmentation of whole human head structures using deep learning.
result Generated head models exhibit strong matching with manual segmentation results.
The paper improves CNN models for polyp segmentation, enhancing their accuracy and interpretability.
problem Uncertainty and interpretability in CNNs for medical image analysis.
method Develop and evaluate recent advances in uncertainty estimation and model interpretability in FCNs for polyp segmentation.
result Highest performing model achieves 76.06% mean IOU accuracy on the EndoScene dataset.
Weakly supervised model segments tumor areas from whole slide images.
problem Training segmentation models on noisy labeled data from whole slide images.
method Online patch sampling and robust KL divergence extension.
result Model successfully segments tumor areas with strong morphological consistency.
Proposes a new method for medical image segmentation.
problem Medical image segmentation challenges.
method Decompose-and-Integrate Learning.
result Improves segmentation performance on multiple datasets.