Learning Latent Stable Patterns for Image Understanding With Weak and Noisy Labels

How to Efficiently Annotate Images for Best-Performing Deep Learning-Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model

Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, the process of preparing datasets for training segmentation DNNs is both labor-intensive and costly, as it typically requires pixel-level annotations for each object of interest. To mitigate this challenge, alternative approaches such as using weak labels (e.g., bounding boxes or scribbles) or less precise (noisy) annotations can be employed. Noisy and weak labels are significantly quicker to generate, allowing for more annotated images within the same time frame. However, the potential decrease in annotation quality may adversely impact the segmentation performance of the resulting model. In this study, we conducted a comprehensive cost-effectiveness evaluation on six variants of annotation strategies (9 ∼ 10 sub-variants in total) across 4 datasets and conclude that the common practice of precisely outlining objects of interest is virtually never the optimal approach when annotation budget is limited. Both noisy and weak annotations showed usage cases that yield similar performance to the perfectly annotated counterpart, yet had significantly better cost-effectiveness. We hope our findings will help researchers be aware of the different available options and use their annotation budgets more efficiently, especially in cases where accurately acquiring labels for target objects is particularly costly. Our code will be made available on https://github.com/yzluka/AnnotationEfficiency2D .

Kernel Entropy Component Analysis-Based Robust Hyperspectral Image Supervised Classification

Recently, the “noisy label" problem has become a hot topic in supervised classification of hyperspectral images (HSI). Nonetheless, how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing. This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI, which consists of the following steps. First, the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability. Then, the eigenvectors and eigenvalues of the kernel matrix can be obtained by employing symmetric matrix decomposition. Next, the entropy corresponding to each training sample in each class is calculated based on entropy component analysis using the eigenvalues arranged in descending order and the corresponding eigenvectors. Finally, the sigmoid function is applied to the entropy of each sample to obtain the probability distribution. Meanwhile, a decision probability threshold is introduced into the above probability distribution to cleanse the noisy labels of training samples with mislabeled samples for each class. The effectiveness of the proposed method is evaluated by support vector machines on several real hyperspectral data sets. The experimental results show that the proposed KECA method is more efficient than other noisy label detection methods in terms of improving performance of the supervised classification of HSI.