Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years. While image restoration methods typically utilize synthetically distorted training data for training, deep quality assessment models often require expensive labeled subjective data. However, recent studies have shown that activations of deep neural networks trained for visual modeling tasks can also be used for perceptual quality assessment of images. Following this intuition, we propose a novel attention-based convolutional neural network capable of simultaneously performing both image restoration and quality assessment. We achieve this by training a JPEG deblocking network augmented with "quality attention" maps and demonstrating state-of-the-art deblocking accuracy, achieving a high correlation of predicted quality with human opinion scores.
Instructional activity recognition is an analytical tool for the observation of classroom education. One of the primary challenges in this domain is dealing with the intricate and heterogeneous interactions between teachers, students, and instructional objects. To address these complex dynamics, we present an innovative activity recognition pipeline designed explicitly for instructional videos, leveraging a multi-semantic attention mechanism. Our novel pipeline uses a transformer network that incorporates several types of instructional semantic attention, including teacher-to-students, students-to-students, teacher-to-object, and students-to-object relationships. This comprehensive approach allows us to classify various interactive activity labels effectively. The effectiveness of our proposed algorithm is demonstrated through its evaluation on our annotated instructional activity dataset.
Of the 50,000 known spider species that taxonomists have identified, a subset of them are considered to be particularly significant due to the harmful physiological effects their venom has on humans, which often demands prompt and precise identification of the spider in emergency scenarios. Traditional spider identification relies on expert knowledge of morphological characteristics for identification, but in critical scenarios this may be inadequate due to time or knowledge constraints. Thanks to the rise of machine learning, we have developed an effective solution to this problem through the testing of powerful deep learning models. In this paper, we utilize various proven image classification models as a backbone, then fine-tune them on a curated dataset of spider images from the citizen science platform iNaturalist with an emphasis on spiders that are particularly harmful to humans. Experimental results are favorable and indicate that modern image classification models perform well on the task of spider species identification. Our highest performing model is a ConvNeXtV2 backbone model which achieves 91.2% accuracy on our testing set. Compared to previous related works, our fine-tuned model is able to achieve higher classification accuracy while handling a much larger number of spider species.
We evaluate the object detection capabilities of deep learning based CNNs on midwave/longwave dual-band infrared (DBIR) video sequences for the first time. The characterization of CNN object detection performance on DBIR data, and in particular comparative analysis of the performance of DBIR systems relative to single-band longwave infrared (LWIR) and midwave infrared (MWIR) systems, has not been reported previously in the open literature. This is due at least in part to a general lack of labeled, publicly available DBIR data sets. In this paper, we apply a well-known, state-of-the-art CNN to DBIR data for the first time. A new labeled DBIR data set was generated comprising multiple classes of vehicles, people, airplanes, and birds. YOLOv4, pre-trained on the MS COCO dataset, was used for inference on the MWIR and LWIR channels of the DBIR sensor independently. The resulting detections from the two bands were considered both separately and jointly. The labeled objects of this DBIR data set were grouped into small, medium, and large classes. Detection performance on the medium and large objects was comparable to YOLOv4 performance reported previously in the open literature for visible wavelength objects in terms of average precision and average recall. Recall performance on small objects showed a significant size-dependent advantage for DBIR over LWIR or MWIR alone.
When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain.
