Graph self-supervised representation learning has gained considerable attention and demonstrated remarkable efficacy in extracting meaningful representations from graphs, particularly in the absence of labeled data. Two representative methods in this domain are graph auto-encoding and graph contrastive learning. However, the former methods primarily focus on global structures, potentially overlooking some fine-grained information during reconstruction. The latter methods emphasize node similarity across correlated views in the embedding space, potentially neglecting the inherent global graph information in the original input space. Moreover, handling incomplete graphs in real-world scenarios, where original features are unavailable for certain nodes, poses challenges for both types of methods. To alleviate these limitations, we integrate masked graph auto-encoding and prototype-aware graph contrastive learning into a unified model to learn node representations in graphs. In our method, we begin by masking a portion of node features and utilize a specific decoding strategy to reconstruct the masked information. This process facilitates the recovery of graphs from a global or macro level and enables handling incomplete graphs easily. Moreover, we treat the masked graph and the original one as a pair of contrasting views, enforcing the alignment and uniformity between their corresponding node representations at a local or micro level. Last, to capture cluster structures from a meso level and learn more discriminative representations, we introduce a prototype-aware clustering consistency loss that is jointly optimized with the preceding two complementary objectives. Extensive experiments conducted on several datasets demonstrate that the proposed method achieves significantly better or competitive performance on downstream tasks, especially for graph clustering, compared with the state-of-the-art methods, showcasing its superiority in enhancing graph representation learning.

Object detection is an essential computer vision task that possesses extensive application prospects in on-road applications. Copious novel methods have been proposed in this branch recently. However, the majority of them have high computational cost, making them intractable to be deployed on embedded devices. In this paper, taking YOLOv5s, the smallest model in the YOLOv5 family, as the baseline, we explore a bag of tricks that improve the detection performance for a specified on-road application, under the premise of ensuring that it does not increase the computational cost of YOLOv5s. Specifically, we introduce relevantly external data to deal with the problems of sample imbalance. Meanwhile, knowledge distillation is employed to transfer knowledge from a cumbersome model to a compact model, where a united distillation scheme is developed to enhance the effectiveness. In addition, a pseudo-label based training strategy is utilized to further learn from the biggest YOLOv5 model. We have applied the above tricks to the Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries held in conjunction with ICMR 2021. The experiments have shown that all the tricks are useful. Their combination have built an accurate and slim detection model. It is highly competitive and has been ranked 2nd place in the competition. We believe the tricks are also meaningful for building other application-oriented object detectors.

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.

This paper investigates the task of automatically associating faces appearing in images (or videos) with their names. Our novelty lies in the use of celebrity Web images to facilitate the task. Specifically, we first propose a method named Image Matching (IM), which uses the faces in images returned from name queries over an image search engine as the gallery set of the names, and a probe face is classified as one of the names, or none of them, according to their matching scores and compatibility characterized by a proposed Assigning-Thresholding (AT) pipeline. Noting IM could provide guidance for association for the well-established Graph-based Association (GA), we further propose two methods that jointly utilize the two kinds of complementary cues. They are: the early fusion of IM and GA (EF-IMGA) that takes the IM score as an additional information source to help the association in GA, and the late fusion of IM and GA (LF-IMGA) that combines the scores from both IM and GA obtained individually to make the association. Evaluations on datasets of captioned news images and Web videos both show the proposed methods, especially the two fused ones, provide significant improvements over GA.

Common substring problems allowing errors are known to be NP-hard. The main challenge of the problems lies in the combinatorial explosion of potential candidates. In this paper, we propose and study a generalized center string (GCS) problem, where not only all models (center strings) of any length, but also the positions of all their (degenerative) instances in input sequences are searched for. Inspired by frequent pattern mining techniques in data mining field, we present an exact and efficient method to solve GCS. First, a highly parallelized Trie-like structure, consensus tree, is proposed. Based on this structure, we present three Bpriori algorithms step by step. Bpriori algorithms can solve GCS with reasonable time and/or space complexities. We have proved that GCS is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both artificial and real data have shown the correctness of the algorithms and the validity of our complexity analysis. A comparison with some current algorithms for solving common approximate substring problems is also given