As few-shot object detectors are often trained with abundant base samples and fine-tuned on novel examples, the learned models usually biased to classes sensitive variance of examples. To address this issue, we propose a meta-learning framework two feature aggregation schemes. More precisely, first present Class-Agnostic Aggregation (CAA) method, where query support features can be aggregated r...

The edge detection model based on deep learning significantly improves performance, but its generally high complexity requires a large pretrained Convolutional Neural Networks (CNNs) backbone, and hence memory computing power. To solve this problem, we carefully choose proper components for detection, introduce Multiscale Aware Fusion Module self-attention feature-unmixing loss function, propos...

Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects relevant image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm a good choice finding evidence edges. It finds point at which function difference properties maximized. This algorithm very general accepts many t...

• We designed a novel architecture for video object detection that capitalizes on temporal information. fusion module to merge feature maps coming from several temporally close frames. proposed an improvement the SpotNet attention module. trained and evaluated our with three different base detectors two traffic surveillance datasets. demonstrated consistent significant of model over baselines. ...

Intrusion detection technologies have grown in popularity recent years using machine learning. The variety of new security attacks are increasing, necessitating the development effective and intelligent countermeasures. existing intrusion system (IDS) uses Signature or Anomaly based systems with learning algorithms to detect malicious activities. Signature-based rely only on signatures that bee...