Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.

Few-shot class-incremental learning (FSCIL) is crucial and practical for artificial intelligence in the real world, which learns novel classes incrementally from few samples without forgetting the previously learned classes. However, FSCIL confronts two significant challenges: “catastrophic forgetting” and “overfitting new.” We focus on convolutional neural network (CNN)-based FSCIL and propose a human cognition-inspired FSCIL method, in which the knowledge of novel classes is learned under the guidance of the previously learned knowledge. Specifically, we learn a discriminative and generalized CNN feature extractor from the base classes in the first task. We generate the representations of base and novel classes in unified feature space without training on novel classes, thus avoiding “forgetting old.” For the novel classes in long sequential tasks, beyond the representation generation, we enhance the representation by exploring the correlations with the previously learned classes to alleviate overfitting new and ensure that the novel classes adapt to the feature space. Experimental results show that our proposed method achieves very competitive results on MiniImageNet and CIFAR-100 datasets.

In this paper, we propose a novel deep learning based deraining method. The proposed method is motivated by the idea that an effective deraining algorithm should have the ability to remove various remaining rain streaks, which have been processed by the deraining method, in a repeated way. So, we design the deraining network in a coarse-to-fine manner that is multi-stage processing procedure and the parameters are shared in each stage. As the spatial contextual information is important for single image deraining, a densely connected dilation convolution block is proposed to deal with rain streaks with different sizes. Moreover, outer dense connections are used to guide the subsequent deraining procedures by fusing all the previous estimated rain-free images. Quantitative and qualitative experimental results demonstrate the superiority of the proposed method compared with recent state-of-the-art deraining methods on Rain100H, Rain1200 and Rain1400 datasets, while the number of parameters of our proposed method is greatly reduced due to the shared parameters strategy.