Facial expression recognition (FER) is a key factor in human behavior analysis. Most algorithms deal with FER as a pure classification problem, assuming that expressions are exclusive to each other. However, we find that in reality, facial expressions are not independent, and they usually share some secondary components which are not fully used by existing methods that handle expression categories mutually exclusively. In this paper, the problem of FER is tackled from a more detailed view: learning to discriminate expressions with consideration of the secondary information. We propose the Secondary Information aware Facial Expression Network ( $SIFE \text{-} Net$ ) to explore the latent components without auxiliary labeling, and we propose a novel dynamic weighting strategy to teach the $SIFE \text{-} Net$ . In contrast to traditional classifiers trained with one-hot labels, the proposed $SIFE \text{-} Net$ takes advantage of secondary expression information and has more rational feature distributions. We carry out extensive experiments and analysis on three widely-used FER datasets, i.e. the CK+ dataset, the JAFFE dataset, and the RAF dataset. Experimental results show that the $SIFE \text{-} Net$ achieves state-of-the-art performance on all three datasets, which demonstrates the effectiveness of our method.

Image registration is a basic task in computer vision, for its wide potential applications in image stitching, stereo vision, motion estimation, and etc. Most current methods achieve image registration by estimating a global homography matrix between candidate images with point-feature-based matching or direct prediction. However, as real-world 3D scenes have point-variant photograph distances (depth), a unified homography matrix is not sufficient to depict the specific pixel-wise relations between two images. Some researchers try to alleviate this problem by predicting multiple homography matrixes for different patches or segmentation areas in images; in this paper, we tackle this problem with further refinement, i.e. matching images with pixel-wise, depth-aware homography estimation. Firstly, we construct an efficient convolutional network, the DPH-Net , to predict the essential parameters causing image deviation, the rotation ( $R$ ) and translation ( $T$ ) of cameras. Then, we feed-in an image depth map for the calculation of initial pixel-wise homography matrixes, which are refined with an online optimization scheme. Finally, with the estimated pixel-specific homography parameters, pixel correspondences between candidate images can be easily computed for registration. Compared with state-of-the-art image registration algorithms, the proposed DPH-Net has the highest performance of 0.912 EPE and 0.977 SSIM, demonstrating the effectiveness of adding depth information and estimating pixel-wise homography into the image registration process.

Nowadays, autonomous vehicles (AVs) have become an excellent solution for alleviating the burden of the traffic system and improving safety. However, the large-scale application of AVs is significantly restrained by the high-power cost of their numerous sensors and processors, which can reach hundreds of watts and occupy most of the vehicular power consumption. To meet such tremendous computation requirements, most AVs rely on vehicle-to-everything (V2X) communication with mobile edge computing (MEC) techniques to relocate their tasks to external computing. In this paper, the problem of optimizing the task and path assignment process in the V2X-MEC system is tackled for further reducing vehicular power consumption with the constraint of transmission latency. We incorporate the Nash Equilibrium (NE) concept for generating the optimal assignment of tasks and paths, and the attractor selection model (ASM) is introduced to flexibly allocate tasks with consideration of different energy-saving requirements. With comprehensive simulation experiments, we show that the proposed method can save approximately 85% energy compared with the method without task assignment, and it is over 9% and 16% better than the shortest path assignment method and the minimum average delay method, respectively. Besides, the power consumption in AVs with low remaining electricity can be further reduced by over 43%.

Challenging due to the absence of color information and low resolution, infrared pedestrian tracking is still of great importance in video surveillance for its ability to distinguish and track persons in night vision. Recent researches show that the CF (Correlation Filters) based methods have a satisfying performance on the infrared pedestrian tracking task when combined with deep features. However, most of these algorithms only rely on the general convolutional features and online model updating to trace object variation, resulting in the lost track of pedestrians with large deformation and infrequent poses. In this paper, we propose a long-term inter-frame graph memory feature as a stronger descriptor for infrared pedestrians. We show that this cross-frame memory graph can enrich deep features with time-variant pedestrian appearance information, boosting the robustness and accuracy of the baseline CF tracker. Extensive experiments and analyses are carried out on the widely-used PTB-TIR dataset, demonstrating the effectiveness and universality of the proposed graph memory features for infrared pedestrians.