Current camera-based infant monitoring mainly focuses on physiological measurement, overlooking its important semantic analysis potential for detecting accidental suffocation caused by oronasal occlusion during sleep. However, developing a robust infant suffocation risk detection model typically requires substantial labeled data, which is very difficult to obtain in real-world scenarios. To address this, we utilized the text-to-image diffusion model to generate diverse infant images depicting oronasal occlusion and non-occlusion scenarios controlled by text prompts. To ease the process of labeling, self- and semi-supervised learning algorithms are leveraged to learn the semantic information from unlabeled data with the support of minimal labeled data to train different model architectures. To evaluate the feasibility of this solution, we conducted a clinical trial in the neonatology department, which collected video data from 22 infants under various oronasal occlusion scenarios using breathable covers (e.g. clinical tissue). The clinical evaluation shows that most models trained on 25,000 generated images achieved over 90% performance on metrics of accuracy, recall, and F1-score, outperforming conventional approaches that pre-train and fine-tune the model using over 90,000 labeled task-related online images. This demonstrates the feasibility of leveraging text-to-image generated data to achieve robust camera-based infant suffocation risk detection, so as to secure the sleep safety of infants. More importantly, it beacons the potential of using text-based large-scale model to solve the general issue of scarcity of human data in artificial intelligence-based healthcare or clinical applications.

Semantic patterns offine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive de-tails carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on ob-ject representation. For discounting pose variations, this paper proposes to learn a novel graph based object rep-resentation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network. Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained ob-ject classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://github.com/yangxhll/P2P-Net.

Learning effective generative models for natural image synthesis is a promising way to reduce the dependence of deep models on massive training data. This work focuses on Fine-Grained Image Synthesis (FGIS) in the semi-supervised setting where a small number of training instances are labeled. Different from generic image synthesis tasks, the available fine-grained data may be inadequate, and the differences among the object categories are typically subtle. To address these issues, we propose a Semantic Regularized class-conditional Generative Adversarial Network, which is referred to as SReGAN. We incorporate an additional discriminator and classifier into the generator-discriminator minimax game. Competing with two discriminators enforces the generator to model both marginal and class-conditional data distributions, which alleviates the problem of limited training data and labels. However, the discriminators may overlook the class separability. To induce the generator to discover the distinctions between classes, we construct semantically congruent and incongruent pairs in the generation process, and further regularize the generator by encouraging high similarities of congruent pairs, while penalizing that of incongruent ones in the classifier's feature space. We have conducted extensive experiments to verify the capability of SReGAN in generating high-fidelity images on a variety of FGIS benchmarks.

Image denoising is about removing measurement noise from input image for better signal-to-noise ratio. In recent years, there has been great progress on the development of data-driven approaches for image denoising, which introduce various techniques and paradigms from machine learning in the design of image denoisers. This paper aims at investigating the application of ensemble learning in image denoising, which combines a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble. Then, with an effective re-sampling scheme, several ensemble-learning-based image denoisers are constructed using different sequential combinations of multiple proposed base denoisers. The experiments showed that sequential ensemble learning can effectively boost the performance of image denoising.

Proview is probably the first Open Source system based on Linux for process control. It is modern, powerful and general. It contains all functions normally required for successful sequential control, adjustment, data acquisition, supervision, communication, etc. But it is not used widely yet. This paper proposes a new Soft PLC (Programmable Logic Controller) solution of automatic burdening system based on Proview. The system adopts advanced DCS (Distributed Control System) structure and PID control to get high precision. The focus is on the principle of process control and system structure in Proview, providing I/O configuration, hardware design circuit, and part of ladder program. Finally, verification is carried out with the performance test of the overall machine.