Video Anomaly Detection (VAD) is crucial for public safety and detecting abnormalities in riskprone zones. Anomaly Detection from weakly-labeled datasets has been very challenging for CCTV surveillance videos. The challenge is more intense when we involve high-altitude drone videos for VAD tasks. There have been very few works for drone-captured VAD; even the existing CCTV VAD methods suffer from several limitations that hinder their optimal performance. The previous works for VAD mostly use single modal data, e.g., video data, which is insufficient to understand the complex scene context. Moreover, the existing multimodal systems use the traditional linear fusion method to capture multimodal feature interaction, which does not address the misalignment issue from different modalities. Next, the existing work relies on fixed-scale video segmentation, which fails to preserve the fine-grained local and global context knowledge. Also, it was found that the feature magnitude-based VAD does not correctly represent the anomalous events. To address these issues, we present a novel vision-language-based video anomaly detection for drone videos. We use adaptive long-short-term video segmentation (ALSVS) for local-global knowledge extraction. Next, we propose to use a shallow yet efficient attention-based feature fusion (AFF) technique for multimodal VAD (MMVAD) tasks. Finally, for the first time, we introduce feature anomaly learning based on a saliency-aware contrastive algorithm. We found contrastive anomaly feature learning is more robust than the magnitude-based loss calculation. We performed experiments on two of the latest drone VAD datasets (Drone-Anomaly and UIT Drone), as well as two CCTV VAD datasets (UCF crime and XD-Violence). Compared to the baseline and closest SOTA, we achieved at least a +3.8% and +3.3% increase in AUC, respectively, for the drone and CCTV datasets.

The recent widespread increase of the Mpox (formerly monkeypox) virus infections in the South Asian and African countries has raised concerns among medical professionals regarding the potential emergence of another pandemic in those regions. With the number of available test kits surpassing the count of positive/probable cases, there is a pressing need to develop a robust and lightweight classifier model to alleviate the burden of physical testing kits and expedite the detection process. The existing state-of-the-art primarily focuses on achieving high accuracy in modeling Mpox without considering factors such as modeling suitability, real-time inferencing, and adaptability to resource-constrained CPU-only mobile devices. In this research, we propose a novel lightweight binarized DarkNet53 model, referred to as BinaryDNet53, which is approximately ∼ 20× more computationally efficient and ∼ 2× more power-efficient than the current state-of-the-art. This model demonstrates smooth detection capabilities when deployed on small hand-held or embedded devices. Our work introduces large-margin feature learning and weighted loss calculation to enhance results, particularly on complex samples. We conduct experiments using the latest MSLD v2.0 dataset, showcasing the superiority of the proposed model over state-of-the-art models based on classification and computational metrics, including Watt power consumption, required memory, and GFLOPS.

State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects from existing satellite benchmarksâ\euro”the proposed method results in up to a 7.4% increase in mAP performance measure over the best state-of-art.Â

Here, we address the challenge of generalizing object detection and labeling in remote sensing data from one dataset to another. It is the variability in different altitudes, geographical variances, weather conditions, and object size across datasets that state-of-the-art DNN largely fails to generalize. Contrastive-based unsupervised domain adaptation attempts to bridge the gap by producing discriminating features for frames and instances across different datasets, and we have shown some progress on the domain alignment on local, global, and instance-aware features for remote sensing data. In this research, we propose using support-guided pseudo-labeling on the target domain instances to enable instance domain adaptation on the source and target datasets. Next, we introduce the contrastive loss function with multiple positive examples to make the model more generalized of the variable appearance of a particular class over images and domains. Also, we proposed debiased contrastive learning based on class probabilities to address the challenge of false negatives in the unsupervised framework. We show the advantages of the proposed model on satellite (DIOR and DOTA2.0) and drone (Visdrone and UAVDT) image datasets.