In this paper, we propose a novel light-weight feature integration and fusion method to enhance the discriminative ability of deep convolutional features for the task of fine-grained vehicle recognition. The proposed method is built on the deep convolutional layers from which the discriminative part features could be integrated and fused accordingly. More specifically, a basic feature integration module is adopted to integrate the feature maps of deep convolutional layers into groups in each of which the related discriminative parts are assembled together. Then a fusion module follows to model the coarse-to-fine relationship of the part features and further ensure the integrity and effectiveness of the part features. We conduct comparison experiments on public dataset, and the results show that the proposed method achieves comparable performance with state-of-the-art algorithms.

Fine-grained vehicle categorization has evolved into a significant subject of study due to its importance in the Intelligent Transportation System. A highly accurate and real-time vehicle categorization system will help to support many applications not only in the security aspect but also many walks of life. In this paper, facing the growing importance of this study, we present an image dataset named Frontal-103 to promote the development of the vision-based research on the vehicle, and particularly for the task of fine-grained vehicle categorization. This paper provides a detailed analysis of Frontal-103 in its current state: 1,759 fine-grained vehicle models in 103 vehicle makes and 65,433 web-nature images in total. Apart from the specific viewpoint and vehicle hierarchy, Frontal-103 is superior to the other state-of-the-art vehicle image datasets not only in the scale and diversity but also the accuracy and fine-grained level. We further discuss the peculiar challenges and issues lies in the task of fine-grained vehicle categorization and illustrate the usefulness of our dataset in addressing those problems. We hope Frontal-103 will be beneficial to the vision-based vehicle analysis and contribute to the computer vision community.

Recent studies show that small perturbations in video frames could misguide single object trackers. However, such attacks have been mainly designed for digital-domain videos (i.e., perturbation on full images), which makes them practically infeasible to evaluate the adversarial vulnerability of trackers in real-world scenarios. Here we made the first step towards physically feasible adversarial attacks against visual tracking in real scenes with a universal patch to camouflage single object trackers. Fundamentally different from physical object detection, the essence of single object tracking lies in the feature matching between the search image and templates, and we therefore specially design the maximum textural discrepancy (MTD), a resolution-invariant and target location-independent feature de-matching loss. The MTD distills global textural information of the template and search images at hierarchical feature scales prior to performing feature attacks. Moreover, we evaluate two shape attacks, the regression dilation and shrinking, to generate stronger and more controllable attacks. Further, we employ a set of transformations to simulate diverse visual tracking scenes in the wild. Experimental results show the effectiveness of the physically feasible attacks on SiamMask and SiamRPN++ visual trackers both in digital and physical scenes.

Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial attacks in a training-free and attack-agnostic manner, this work proposes a novel and effective reconstruction-based defense framework by delving into deep image prior (DIP). Fundamentally different from existing reconstruction-based defenses, the proposed method analyzes and explicitly incorporates the model decision process into our defense. Given an adversarial image, firstly we map its reconstructed images during DIP optimization to the model decision space, where cross-boundary images can be detected and on-boundary images can be further localized. Then, adversarial noise is purified by perturbing on-boundary images along the reverse direction to the adversarial image. Finally, on-manifold images are stitched to construct an image that can be correctly predicted by the victim classifier. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art reconstruction-based methods both in defending white-box attacks and defense-aware attacks. Moreover, the proposed method can maintain a high visual quality during adversarial image reconstruction.

In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral-spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption.