A novel feedbackward approach for semantic segmentation reduces parameter count.
problem Semantic segmentation challenges in terms of model complexity and performance.
method Reverse-direction encoder-decoder architecture using the same network for both encoding and decoding.
result Significantly outperforms other models using only 13 VGG-16 layers, achieving higher IoU scores.
Paper tackles handwritten annotation recognition in historic documents using FCNN.
problem Recognizing handwritten annotations in challenging historic German documents.
method End-to-end semantic segmentation using Fully Convolutional Neural Networks (FCNN).
result Best model achieves 95.6% IoU score on test documents.
IoU is not submodular, impacting machine learning applications.
problem IoU's submodularity assumption in machine learning is incorrect.
method Demonstrated through counterexamples and analysis of function properties.
result IoU is not a submodular function, invalidating its use in certain applications.
Corrects a claim about IoU loss being submodular, showing it's true.
problem Clarifies the submodularity of IoU loss function.
method Analyzes the misreading of set membership in previous claims.
result Demonstrates that the previous claims about IoU loss are incorrect.
A new framework for consistent segmentation evaluation reduces operating losses.
problem Inconsistent thresholding-based segmentation methods lead to suboptimal solutions.
method Developed a consistent ranking-based framework (RankDice/RankIoU) using Bayes rules and Dice-/IoU-calibration.
result The proposed framework is Dice-/IoU-calibrated and provides excess risk bounds and convergence rates.
Optical flow enhances semantic segmentation for automated driving.
problem Improving semantic segmentation for automated driving systems.
method Constructed four architectures using RGB, flow, RGBF, and two-stream RGB + flow.
result Two-stream architecture achieves best results with 4% IoU improvement.
RankSEG-RMA improves semantic segmentation efficiency and applicability.
problem Inconsistent or suboptimal semantic segmentation results due to argmax or thresholding.
method Developed RankSEG-RMA using reciprocal moment approximation to optimize Dice and IoU metrics.
result RankSEG-RMA reduces computational complexity to O(d) while maintaining comparable performance.
Method meta-classifies semantic segmentation predictions using dispersion measures.
problem Assessing the quality of semantic segmentation predictions.
method Aggregates dispersion measures (entropy) of predicted probabilities to derive metrics correlated with IoU.
result Metrics correlate well with IoU, providing reliable prediction quality ratings.
YOLOv3 detects ships in real-time with high accuracy.
problem Real-time target detection in maritime scenarios.
method YOLOv3 model trained on a large dataset of marine vessels.
result Average Precision up to 96% for IoU of 0.5.
Automated image segmentation distinguishes overlapping human chromosomes.
problem Distinguishing overlapping human chromosomes for medical diagnostics.
method Customized convolutional neural network for image segmentation.
result IOU scores of 94.7% for overlapping regions, 88-94% for non-overlapping regions.
A benchmark evaluates ioUS-to-MR synthesis methods for brain tumor surgery.
problem Difficult interpretation of ioUS images for brain tumor surgery.
method Six generators trained under four inference regimes and two targets on public data.
result SynDiff-2.5D best preserved downstream segmentation (U_Dice=0.55).
Method generates uncertainty measures for street scene segmentation.
problem Reliability and uncertainty measures in semantic segmentation of street scenes.
method Nested crops, neural network segmentation, post-processing, uncertainty heat maps.
result Significant improvements in classification and regression performance.
SCENE-Net improves 3D point cloud segmentation with low resource usage and transparency.
problem Lack of resources and transparency in 3D semantic segmentation models.
method SCENE-Net uses signature shapes identified via GENEOs to achieve semantic segmentation with minimal resources.
result SCENE-Net achieves comparable IoU to state-of-the-art methods with less data and computational resources.
ACNN improves medical image segmentation with full resolution and higher IoUs.
problem Reduced spatial resolution in current DCNNs hinders medical image segmentation.
method Proposes ACNN using atrous convolution, cascaded atrous II-blocks, residual learning, and Instance Normalization.
result ACNN achieves higher IoUs than U-Net and Deeplabv3+ with fewer parameters.
AI tool automates blood segmentation from head CT scans after SAH.
problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.
method Transformer-based Swin UNETR architecture for noncontrast CT scans.
result High accuracy and robust performance across internal and external validation cohorts.
TokenCut detects and segments objects in images and videos without supervision.
problem Detecting and segmenting salient objects in images and videos without labeled data.
method Graph-based approach using self-supervised transformer features and Normalized Cut algorithm.
result Achieves state-of-the-art results on various detection and segmentation tasks.
Algorithm generates adaptive confidence sets for instance segmentation with guaranteed coverage.
problem Uncalibrated predictions and lack of uncertainty quantification in instance segmentation models.
method Conformal prediction algorithm to generate adaptive confidence sets with provable guarantees.
result Empirically, prediction sets vary in size based on query difficulty and attain target coverage, outperforming baselines.
UNet++ improves medical image segmentation with deep supervision.
problem Improving accuracy in medical image segmentation.
method Nested U-Net architecture with deep supervision.
result UNet++ achieves significant improvements in IoU scores.
Z-Net improves 3D CT volume segmentation for surgical planning.
problem Discontinuities and class-imbalances in 3D CT volume segmentation.
method Z-Net uses anisotropic spatial separable convolutions to preserve full field-of-view.
result Z-Net achieves up to 12.6% improvement in IoU for CT segmentation.
GMM uses mmWave radar to classify traffic modes in poor lighting.
problem Classifying traffic modes in poor lighting conditions.
method GMM on mmWave radar point clouds.
result Good segmentation performance in pedestrian and car classification.
CRAUM-Net improves salient object detection with context and uncertainty modeling.
problem Accurate salient object detection with precise boundary delineation.
method Contextual Recursive Attention with Uncertainty Modeling, multi-scale context aggregation, attention mechanisms, edge-aware decoder, Monte Carlo Dropout.
result Superior performance in producing accurate and reliable saliency maps.
New unbiased variance estimator for random forests using Hoeffding decomposition.
problem Uncertainty quantification in random forests with large kernel sizes and small sample sizes.
method Proposes a new Hoeffding decomposition view for variance estimation, establishing unbiased estimators and ratio consistency.
result Establishes the ratio consistency of the proposed variance estimator, justifying confidence interval coverage rates.
Transformer-based method discovers objects from images without labels.
problem Discovering objects in images without labeled data.
method Graph-based approach using self-supervised transformer features and normalized graph-cut.
result Significantly boosts performance in unsupervised object discovery.
New risk control method for non-monotonic losses in complex parameters.
problem Controlling risk for non-monotonic losses with multidimensional parameters.
method Stability-based guarantees for generic algorithms applied to non-monotonic losses.
result Guarantees depend on algorithm stability, with looser guarantees for unstable algorithms.
Bayesian variational inference improves medical image segmentation confidence.
problem Improving interpretability and confidence in deep learning models for medical image segmentation.
method Encoder-decoder architecture based on variational inference for segmenting brain tumor images.
result The model segments brain tumors with both aleatoric and epistemic uncertainty.
Simple method improves deep classifier accuracy under noisy labels.
problem Training deep classifiers with noisy labels.
method Probabilistic approach using temperature parameterized softmax.
result Improves accuracy, log-likelihood and calibration on noisy datasets.
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.
AMI-Net+ tackles medical diagnosis from incomplete, imbalanced data.
problem Medical diagnosis from incomplete and imbalanced data.
method AMI-Net+ uses multi-instance neural network with embedding, multi-head attention, and gated attention-based pooling. It also employs focal loss and self-adaptive multi-instance pooling.
result AMI-Net+ outperforms state-of-the-art models on real-world medical datasets.
CBDA improves active learning for semantic segmentation, especially with imbalanced classes.
problem Class imbalance degrades performance in domain adaptive active learning.
method Class Balanced Dynamic Acquisition (CBDA) selects more balanced labels for active learning.
result CBDA increases minority class performance and outperforms baselines by 0.6-2.4 mIoU.
New method mitigates bias without sensitive data using causal graph and variational autoencoder.
problem Lack of fairness strategies when sensitive attributes are not collected.
method SRCVAE framework based on causal graph for inferring a proxy sensitive attribute.
result Significant improvements in fairness metrics over existing methods.
RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
Generative models' evaluation scores can be misleading, leading to inflated grades.
problem Misleading evaluation scores for generative models.
method Analyzed and compared various scores for evaluating synthetic vs. ground-truth data.
result The Eden score avoids grade inflation and better aligns with human perception.
Improves score estimation for noised targets using known clean scores.
problem Poor score estimation at low noise levels in Denoising Score Matching.
method Introduces Target Score Identity and Target Score Matching loss.
result Score estimates are more accurate at low noise levels.
This work improves likelihood of score-based diffusion ODEs using high-order denoising score matching.
problem The gap between maximum likelihood and score matching objectives for score-based diffusion ODEs.
method High-order denoising score matching to maximize likelihood.
result Score-based diffusion ODEs achieve better likelihood on synthetic and CIFAR-10 data.
Proposes MSFCN and RFCN for video semantic segmentation improving accuracy by 9-15%.
problem Lack of temporal information in semantic segmentation algorithms for videos.
method Integrates motion cues and temporal consistency using recurrent and multi-stream FCN architectures.
result MSFCN-3 achieves significant accuracy improvements in various datasets.
Study compares multivariate scoring rules for distribution forecasts.
problem Evaluating the discrimination ability of multivariate scoring rules.
method Simulation study comparing energy and variogram scores using historical data.
result Variogram score with p=0.5 outperforms other scores.
A new method improves data generation quality by correcting score mismatches.
problem Score mismatch issue in conditional score-based data generation methods.
method Denoising Likelihood Score Matching (DLSM) loss for classifier training.
result The proposed method outperforms previous methods on Cifar-10 and Cifar-100 benchmarks.
New scoring rules improve probabilistic classification model evaluation.
problem Traditional scoring rules misalign with the preference for correct classifications.
method Introduces Penalized Brier Score (PBS) and Penalized Logarithmic Loss (PLL) to modify proper scoring rules.
result PBS and PLL better identify optimal checkpoints and early stopping points, leading to superior F1 scores.
Extends denoising and score estimation to energy models via Tweedie's formula.
problem Linking denoising and score estimation for a wider range of distributions.
method Derives a fundamental identity connecting energy score derivatives and scores.
result Establishes a new identity for energy scores analogous to Tweedie's formula.
Mixed-SCORE+ improves community detection in weak signal networks.
problem Detecting communities in weak signal networks.
method Proposes Mixed-SCORE+ combining properties of Mixed-SCORE and SCORE+.
result Significantly improves detection error rates on Polblogs and weak signal networks.
Examining ESG scoring method for reliability.
problem Reliability of ESG scoring methodology.
method Analyzing Refinitiv's ESG scoring process.
result Methodology needs improvement for trustworthiness.
New scoring rules for multivariate distributions and level sets.
problem Evaluating forecast accuracy for multivariate distributions and level sets.
method Theoretical framework for scoring rules, decomposition of multivariate scoring functions, numerical algorithm for computation.
result New scoring functions for multivariate distributions and level sets, including density and cumulative distribution level sets.
Classifies intrinsically linked tournaments by their score sequences.
problem Classifying intrinsically linked tournaments using their score sequences.
method Examining the score sequences of tournaments and identifying linkless sequences.
result The vast majority of score sequences for 8-vertex tournaments are linkless.
A new method for estimating complex models and high-dimensional data.
problem Difficulty in computing Hessian of log-density functions for complex models and high-dimensional data.
method Sliced score matching, which projects scores onto random vectors before comparison.
result Sliced score matching can learn deep energy-based models and produce accurate score estimates.
Improved score matching methods for estimating score functions and Hessians without high dimensionality.
problem Estimating score functions and Hessians efficiently in high-dimensional data.
method Implicit score matching and denoising score matching, leveraging Gagliardo-Nirenberg inequalities.
result Achieves convergence rates similar to denoising score matching and estimates Hessians without dimensionality issues.
Map projections can reverse district compactness scores, affecting fairness evaluations.
problem District compactness scores can be reordered by map projections, impacting fairness evaluations.
method Mathematical proof and empirical demonstration of map projection effects on compactness scores.
result Map projections can reverse the order of compactness scores, altering fairness evaluations.
Two-stage scoring approach for P2P lending improves loan profitability prediction.
problem Class imbalance and lack of profitability prediction in existing scoring methods.
method Integrates credit scoring and profit scoring using wide and deep learning.
result Two-stage scoring approach outperforms existing methods in loan profitability prediction.
The statistical leverage scores of a complex matrix A∈Cn×d record the degree of alignment between col(A) and the coordinate axes in Cn. These score are used in random sampling algorithms for solving certain numerical linear algebra problems. In this paper we present a max-plus algebr…