Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data perturbation technique to facilitate weak-to-strong consistency learning. However, these techniques often lead to a decrease in the accuracy of synthetic labels corresponding to the synthetic data and introduce excessive perturbations to the distribution of the training data. Such over-perturbation causes the data distribution to stray from its true distribution, thereby impairing the model's generalization capabilities as it learns the decision boundaries. We propose a weak-to-strong consistency learning framework that integrally addresses these issues with two primary designs: 1) it emphasizes the use of highly reliable data to enhance the quality of labels in synthetic datasets through cross-copy-pasting between labeled and unlabeled datasets; 2) it employs uncertainty estimation and foreground region constraints to meticulously filter the regions for copy-pasting, thus the copy-paste technique implemented introduces a beneficial perturbation to the training data distribution. Our framework expands the copy-paste method by addressing its inherent limitations, and amplifying the potential of data perturbations for consistency learning. We extensively validated our model using six publicly available medical image segmentation datasets across different diagnostic tasks, including the segmentation of cardiac structures, prostate structures, brain structures, skin lesions, and gastrointestinal polyps. The results demonstrate that our method significantly outperforms state-of-the-art models. For instance, on the PROMISE12 dataset for the prostate structure segmentation task, using only 10% labeled data, our method achieves a 15.31% higher Dice score compared to the baseline models. Our experimental code will be made publicly available at https://github.com/slhuang24/RCP4CL.

Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as diabetes and obesity. While mainstream food recognition methods excel in scenarios with large-scale annotated datasets, they falter in few-shot regimes where data is limited. This paper addresses this challenge by introducing a variational generative method, the Multivariate Knowledge-guided Variational AutoEncoder (MK-VAE), for few-shot food recognition. MK-VAE leverages handcrafted features and semantic embeddings as multivariate prior knowledge to strengthen feature learning and feature generation in different phases. Specifically, we design a lightweight and flexible feature distillation module that distills handcrafted features to enhance the feature learning network for capturing the salient visual information in few-shot samples. During the feature generation phase, we utilize a variational autoencoder to learn the difference distribution of food data and explicitly boost the latent representation with category-level semantic embeddings to pull homogeneous features closer together while pushing inhomogeneous features apart. Experimental results demonstrate that our proposed MK-VAE significantly outperforms state-of-the-art few-shot food recognition methods in both 5-way 1-shot and 5-way 5-shot settings on three widely-used benchmark datasets: Food-101, VIREO Food-172, and UECFood-256.

In the realm of Zero-Shot Learning (ZSL), we address biases in Generalized Zero-Shot Learning (GZSL) models, which favor seen data. To counter this, we introduce an end-to-end generative GZSL framework called D3GZSL. This framework respects seen and synthesized unseen data as in-distribution and out-of-distribution data, respectively, for a more balanced model. D3GZSL comprises two core modules: in-distribution dual space distillation (ID2SD) and out-of-distribution batch distillation (O2DBD). ID2SD aligns teacher-student outcomes in embedding and label spaces, enhancing learning coherence. O2DBD introduces low-dimensional out-of-distribution representations per batch sample, capturing shared structures between seen and un seen categories. Our approach demonstrates its effectiveness across established GZSL benchmarks, seamlessly integrating into mainstream generative frameworks. Extensive experiments consistently showcase that D3GZSL elevates the performance of existing generative GZSL methods, under scoring its potential to refine zero-shot learning practices. The code is available at: https://github.com/PJBQ/D3GZSL.git

Pavement distress classification is crucial for the maintenance of highways. Although many methods for classifying pavement distress are available, they all assume that training and testing datasets are drawn from the same distribution. When we introduce a new unlabeled dataset with a different distribution, the performance of existing methods decreases considerably due to domain shift, motivating us to look beyond the supervised setting to utilize unlabeled datasets directly in training a model. Therefore, we develop a novel unsupervised domain adaptation (UDA) framework, namely, the Self-supervised Adversarial Network (SSAN) for the first time in this study to conduct multi-category pavement distress classification on an unlabeled target domain. In particular, SSAN leverages adversarial domain adaptation (ADA) thoughts to align the features of different domains. However, distress typically occupies a small Section of high-resolution pavement images. Consequently, aligning features directly is unreasonable because the aligning procedure is still dominated by background features instead of foreground features, which are the most useful information for classification. Therefore, we design a pretext module, called Self-supervised Learning for the Target domain (SLT), to mine foreground information. To validate our method, we use two challenging pavement crack datasets, namely, the Chonqing University Bituminous Pavement Disease Detection (CQU-BPDD) and the Chongqing University Bituminous Pavement Multi-label Disease Detection (CQU-BPMDD) datasets. Moreover, extensive experiments demonstrate that SSAN outperforms state-of-the-art UDA methods.

In this paper, we present a novel face alignment method using a two-level cascaded auto-encoder networks (2-LCAN). In our framework, the first level auto-encoder networks generate rough facial landmarks locations by taking detected face images with low-resolution as inputs. The second level autoencoder networks are constructed by cascading several sub stacked auto-encoder networks (SSAN) in a coarse-to-fine manner. Each SSAN extracts SIFT features and local pixels features around current landmark positions, then fuses them together to further refine landmarks of different facial components with higher image resolutions. Finally, experimental results on LFPW and HELEN datasets demonstrate that our proposed method is significantly superior to the compared approaches both in accuracy and robustness.