Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.

Multisource fusion localization is a mainstream scheme for acquiring accurate locations in complex indoor scenes. To overcome the interference of indoor structures on radio and illumination variation on visual features, the semantic maps provide an effective way for multisource fusion localization. However, due to the lack of visual depth information, solutions of indoor semantic maps suffer from large semantic segmentation errors for similar objects, which leads to the unstable performance of localization systems. To overcome the issue in semantic and fusion localization, we develop a localization system to demonstrate the use of restudy semantic map and self-adapting fusion localization would achieve centimeter-level positioning accuracy, termed VISEL. VISEL uses the proposed spatial attention-aware semantic model to enhance the discrimination of semantic features for capturing accurate semantic maps. On the basis of high-precision semantic maps, VISEL completes an enhanced particle filter fusion localization module with adaptive reassign weight to different localization modules, which successfully improves accuracy through complementary advantages between different signals while overcoming the drawbacks of each signal and interference of complex environment. The extensive experimental results show that VISEL outperforms current state-of-the-art positioning systems and achieves an average positioning accuracy of 0.4 m. VISEL utilizes semantic maps with depth features and enhanced particle filter to reduce the fusion localization error by 38%, which suggests the high-precision semantic maps with depth features could provide a robust solution for the fusion localization system for indoor complex scenes.

Metric learning aims to project original data into a new space, where data points can be classified more accurately using kNN or similar types of classification algorithms. To avoid trivial learning results such as indistinguishably projecting the data onto a line, many existing approaches formulate metric learning as a constrained optimization problem, like finding a metric that minimizes the distance between data points from the same class, with a constraint of ensuring a certain separation for data points from different classes, and then they approximate the optimal solution to the constrained optimization in an iterative way. In order to improve the classification accuracy as much as possible, we try to find a metric that is able to minimize the intra-class distance and maximize the inter-class distance simultaneously. Towards this, we formulate metric learning as a penalized optimization problem, and provide design guideline, paradigms with a general formula, as well as two representative instantiations for the penalty term. In addition, we provide an analytical solution for the penalized optimization, with which costly computation can be avoid, and more importantly, there is no need to worry about the convergence rates or approximation ratios any more. Extensive experiments on real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach.

Due to the lack of the labeled data and the complex structures of various data, it is very hard to learn the uncertainty and representativeness accurately in active learning. In this paper, we propose a multiple kernel active learning framework that incorporates a group regularizer of distribution information into the estimation of uncertainty. The proposed method takes the advantage of multiple kernel learning to learn the kernel space in which the complex structures can be well captured by kernel weights. Meanwhile, we have developed an efficient optimization algorithm to solve the proposed method. Experimental results on twelve UCI benchmark data sets and eight subsets of ImageNet show that the proposed method outperforms several state-of-the-art active learning methods. Moreover, we also have applied the proposed method to multiple feature scenario on Caltech101, and the promising results are also obtained compared with single feature scenario.

In the information explosion era, people only want to access the news information that they are interested in. News broadcast story segmentation is strongly needed, which is an essential basis for personalized delivery and short video. The existing advanced story boundary segmentation methods utilize semantic similarity of subtitles, thus entailing complex semantic computation. The title texts of news broadcast programs include headline (or primary) captions, dialogue captions and the channel logo, while the same story clips only render one primary caption in most news broadcast. Inspired by this fact, we propose a simple method for story segmentation based on the primary caption, which combines YOLOv3 based primary caption extraction and preliminary location of boundaries. In particular, we introduce mean hash to achieve the fast and reliable comparison for detected small-size primary caption blocks. We further incorporate scene recognition to exact the preliminary boundaries, because the primary captions always appear later than the story boundary. Experimental results on two Chinese news broadcast datasets show that our method enjoys high accuracy in terms of R, P and F1-measures.

With the development of the Internet, multimedia data grows by an exponential level. The demand for video organization, summarization and retrieval has been increasing where scene detection plays an essential role. Existing shot clustering algorithms for scene detection usually treat temporal shot sequence as unconstrained data. The graph based scene detection methods can locate the scene boundaries by taking the temporal relation among shots into account, while most of them only rely on low-level features to determine whether the connected shot pairs are similar or not. The optimized algorithms considering temporal sequence of shots or combining multi-modal features will bring parameter trouble and computational burden. In this paper, we propose a novel temporal clustering method based on graph convolution network and the link transitivity of shot nodes, without involving complicated steps and prior parameter setting such as the number of clusters. In particular, the graph convolution network is used to predict the link possibility of node pairs that are close in temporal sequence. The shots are then clustered into scene segments by merging all possible links. Experimental results on BBC and OVSD datasets show that our approach is more robust and effective than the comparison methods in terms of F1-score.