Skeleton-based action recognition is vital for numerous real-world applications, yet it continues to face challenges due to limited and imbalanced 3D motion data, complex movement dynamics, and sensitivity to viewpoint variations. To tackle these issues, we introduce a novel co-learning framework that unifies generative and discriminative modeling by combining a 3D Motion Diffusion Model (MDM) with its inverse counterpart, I-MDM, for more robust recognition. Through the incorporation of high-quality synthetic motions guided by discriminative features from I-MDM, our method achieves state-of-the-art top-1 accuracy on Hu-manAct12 (94.63%) and NTU-13 (99.25%), while delivering over three times faster inference (5.32 ms) than prior methods. These results highlight the potential of diffusion-based generative augmentation guided by discriminative feedback, paving a new path for efficient and accurate skeleton-based action recognition. The code is available at https://github.com/knu-vis/I-MDM.

Detecting illegal activities using video anomaly detection is an enormous challenge in security and surveillance. The lack of labeled instances for anomalous actions poses a significant obstacle to existing learning techniques, and determining the optimal data representation that captures the essential features and patterns vital for detecting anomalies proves to be exceedingly difficult. We have developed a few-shot video anomaly detection method, FewVAD, which employs a key-frame selection module and spatial-temporal relational modeling to extract pertinent features and reduce temporal redundancy from lengthy surveillance recordings. We have evaluated our method on two popular surveillance datasets, UCF-Crime and XD-Violence, and compared its performance against established few-shot models and other unsupervised and weakly supervised learning video anomaly detection models. Our model has attained an accuracy of 41.7% and 54.3% for 5-way 5-shot few-shot configuration on the UCF-Crime and XD-Violence datasets, respectively. Furthermore, it has obtained an AUC score of 86.60% for the 2-way anomaly detection task on the UCFCrime dataset. FewVAD achieves a milestone in few-shot video anomaly detection, competing strongly with current weakly-supervised and unsupervised VAD methods.

Fast and affordable solutions for COVID-19 testing are necessary to contain the spread of the global pandemic and help relieve the burden on medical facilities.Currently, limited testing locations and expensive equipment pose difficulties for individuals seeking testing, especially in low-resource settings.Researchers have successfully presented models for detecting COVID-19 infection status using audio samples recorded in clinical settings, suggesting that audio-based Artificial Intelligence models can be used to identify COVID-19.Such models have the potential to be deployed on smartphones for fast, widespread, and low-resource testing.However, while previous studies have trained models on cleaned audio samples collected mainly from clinical settings, audio samples collected from average smartphones may yield suboptimal quality data that is different from the clean data that models were trained on.This discrepancy may add a bias that affects COVID-19 status predictions.To tackle this issue, we propose a multi-branch deep learning network that is trained and tested on crowdsourced data where most of the data has not been manually processed and cleaned.Furthermore, the model achieves state-of-art results for the COUGHVID dataset.After breaking down results for each category, we have shown an AUC of 0.99 for audio samples with COVID-19 positive labels.

Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

MOTIVATION: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation. RESULTS: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015. AVAILABILITY AND IMPLEMENTATION: https://github.com/ahmed-fakhry/dive CONTACT: : [email protected].

The quality of a software architecture is largely dependent upon the underlying architectural decisions at the framework, tactic, and pattern levels. Decisions to adopt certain solutions determine the extent to which desired qualities such as security, availability, and performance are achieved in the delivered system. In this tool demo, we present our Eclipse plug-in named Archie as a solution for maintaining architectural qualities in the design and code despite long-term maintenance and evolution activities. Archie detects architectural tactics such as heartbeat, resource pooling, and role-based access control (RBAC) in the source code of a project; constructs traceability links between the tactics, design models, rationales and source code; and then uses these to monitor the environment for architecturally significant changes and to keep developers informed of underlying design decisions and their associated rationales.