Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight-or gradient-based federated learning) by over 1.4× on federated datasets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.

CNN-based face detection methods have achieved significant progress in recent years. However, for high performance face detection, there are still many challenging problems, e.g., the speed-accuracy balance and the performance degradation in adverse conditions. In this paper, by taking advantage of the characteristic of CNN, we propose an effective anchor generation and bounding-box regression method that can make a good balance between speed and accuracy, and also work well in bad conditions. The classic structure of CNN produces pyramid-like feature maps due to the pooling or other downscale operations. According to the size of a feature map, we divide the image into grids. Each grid corresponds to a point in the feature map. We make the corresponding feature point responsible for identifying the content of the grid. If this grid area belongs to the face area, it is a natural anchor for face bounding-box regression. Since this anchor is square, it is reasonable to use it to predict the face bounding-box which is square-like. The points in the lower-level feature map correspond to smaller grids, which are dedicated to predicting the bounding-boxes of smaller faces. The points in the higher-level feature maps correspond to larger grids, which are responsible for predicting the bounding-boxes of larger faces. Hence our method can effectively detect multi-scale faces. With this effectiveness, our method can achieve a high detection accuracy using fewer parameters which leads to a fast detection speed. The experiments demonstrate the effectiveness of our method.

CNN-based face detection methods have achieved significant progress in recent years. However, making a good balance between time cost and detection accuracy is still a challenging problem. Those methods which can reach a very high detection accuracy always have complicated networks and rely on expensive GPUs for inference, while those methods which have shallow networks and can run on common devices always lose detection accuracy to a large extent. In this paper, we propose an effective anchor generation and bounding-box regression method which can improve the detection accuracy by modifying the detection head of the popular detection networks. With this effectiveness, we can reduce the trainable weights of the network to speed up the inference while maintaining high accuracy. As a result, our method can get a better speed-accuracy balance. In our method, we divide the input image into grids according to the sizes of the pyramid-like feature maps produced by CNN. In training, those grids close to the center of the ground-truth bounding-boxes are selected as anchors. After training, the regression mapping from the anchors to the ground-truth bounding-boxes can be acquired by the exponential transformation we designed. Our method explicitly and strictly use feature maps in different levels to detect faces of different sizes. The higher-level feature maps and larger grid anchors are responsible for detecting larger faces, while the lower-level feature maps and smaller grid anchors are dedicated to detecting smaller faces. Therefore, our method is effective for detecting multi-scale faces. The experiments on both GPU and CPU demonstrate that our method is effective. Our source code is publicly available on https://github.com/zhouliguo/GAFace.

The collection of users’ near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.

Since the surveillance cameras are usually mounted at a high position to overlook targets, tilt-angle faces on overhead view are common in the public video surveillance environment. Face recognition approaches based on deep learning models have achieved excellent performance, but there remains a large gap for the overlooking surveillance scenarios. The results of face recognition depend not only on the structure of the model, but also on the completeness and diversity of the training samples. The existing multi-pose face datasets do not cover complete top-view face samples, and the models trained by them thus cannot provide satisfactory accuracy. To this end, this paper pioneers a multi-view tilt-angle face dataset (TFD), which is collected with an elaborately devised overhead capture equipment. TFD contains 11,124 face images from 927 subjects, covering a variety of tilt angles on the overhead view. To verify the validity of the constructed dataset, we further conduct comprehensive face detection and recognition experiments using the corresponding models trained by WiderFace, Webface and our TFD, respectively. Experimental results show that our TFD substantially promotes the face detection and recognition accuracy under the top-view situation. TFD is available at https://github.com/huang1204510135/D FD.

With the prosperous development of Convolutional Neural Networks, currently they can perform excellently on visual understanding tasks when the input images are high quality and common quality images. However, large degradation in performance always occur when the input images are low quality images. In this paper, we propose a new super-resolution method in order to improve the classification performance for low-resolution images. In an image, the regions in which pixel values vary dramatically contain more abundant high frequency contents compared to other parts. Based on this fact, we design a weight map and integrate it with a super-resolution CNN training framework. During the process of training, this weight map can find out positions of the high frequency pixels in ground truth high-resolution images. After that, the pixel-level loss function takes effect only at these found positions to minimize the difference between reconstructed high-resolution images and ground truth high-resolution images. Compared with other state-of-the-art super-resolution methods, the experiment results show that our method can recover more high frequency contents in high-resolution image reconstructing, and better improve the classification accuracy after low-resolution image preprocessing.

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.

Convolutional neural networks (CNNs) have wide applications in pattern recognition and image processing. Despite recent advances, much remains to be done for CNNs to learn a better representation of image samples. Therefore, constant optimizations should be provided on CNNs. To achieve a good performance on classification, intuitively, samples' interclass separability, or intraclass compactness should be simultaneously maximized. Accordingly, in this paper, we propose a new network, named separability and compactness network (SCNet) to rectify this problem. SCNet minimizes the softmax loss and the distance between features of samples from the same class under a jointly supervised framework, resulting in simultaneous maximization of interclass separability and intraclass compactness of samples. Furthermore, considering the convenience and the efficiency of the cosine similarity in face recognition tasks, we incorporate it into SCNet's distance metric to enable sample features from the same class to line up in the same direction and those from different classes to have a large angle of separation. We apply SCNet to three different tasks: visual classification, face recognition, and image superresolution. Experiments on both public data sets and real-world satellite images validate the effectiveness of our SCNet.

Convolutional neural networks (CNNs) have shown great advantages in computer vision fields, and loss functions are of great significance to their gradient descent algorithms. Softmax loss, a combination of cross-entropy loss and Softmax function, is the most commonly used one for CNNs. Hence, it can continuously increase the discernibility of sample features in classification tasks. Intuitively, to promote the discrimination of CNNs, the learned features are desirable when the inter-class separability and intra-class compactness are maximized simultaneously. Since Softmax loss hardly motivates this inter-class separability and intra-class compactness simultaneously and explicitly, we propose a new method to achieve this simultaneous maximization. This method minimizes the distance between features of homogeneous samples along with Softmax loss and thus improves CNNs' performance on vision-related tasks. Experiments on both visual classification and face verification datasets validate the effectiveness and advantages of our method.

Airborne light detection and ranging (LiDAR) remote sensing enables accurate estimation and monitoring of terrain and vegetation, and digital surface model (DSM) and digital elevation model (DEM) are vital analytical tools to achieve this estimation and monitoring. Among them, DSM can be directly acquired from airborne LiDAR point clouds; nevertheless, for the production of DEM, point clouds representing a surface of ground objects should be accurately filtered out at first. In some mountain forest areas, due to the limited penetration of airborne LiDAR, ground points sustain a serious lack, which results in the difficulty in producing accurate DEMs. To reduce the intricacy and subjectivity caused by the manual supplement to ground points, this letter proposes a new DEM retrieval method from airborne LiDAR point clouds in mountain areas based on deep neural networks (DNNs). With a DNN model trained by accurate DEMs and DSMs, DEM retrieval becomes much easier by inputting their DSM into this model for prediction. Experiments on Fujian and Hainan mountain data sets demonstrate the effectiveness of this supervised method.

Video satellite imagery is a new technique for earth dynamic observation and has a wide range of uses in environmental fields. Despite its capability of dynamic targets' detection, it sustains a serious restriction of the image quality due to the degradation and compression in its imaging process. Hence, the super-resolution (SR) reconstruction on these compressed low-spatial-resolution images is of significance to afterward ground objects recognition and detection tasks. Based on the recent proposed state-of-the-art convolutional neural networks (CNNs) SR methods, we proposed an SR method which could get more precise reconstructed high-spatial-resolution images. Trained with Gaofen-2 satellite images, a robust CNN model specified in satellite image SR is obtained. Experimentally, the reconstruction results on Jilin-1 mission satellite images validate the effectiveness of our method.

With the ability of representing 3D scene geometry, depth maps are used to synthesize virtual views in free view video (FVV) or 3DTV. However, compression artefact of the depth images always lead to seriously geometry distortions in synthesized view, which severely affects the visual perception of 3D display. To solve the problem caused by depth artifact, the bilateral filter based method is presented to alleviate the noise by local weighted sum of neighboring pixels. However, to overcome the unstable feature of local weight of noisy pixels, we propose a novel graph cuts algorithm for depth filter with the constraint of corresponding structure information in color image. As a depth in-loop filter, the filter is incorporated into the framework of H.264/MVC. The proposed approach offers 0.5dB and 0.8dB average PSNR gains in terms of video rendering quality and depth coding efficiency comparing with the state-of-the-art.