Recently, Semantic Change Detection (SCD) has gained growing attention from the Remote Sensing (RS) research community due to its critical role in Earth observation applications. Typical approaches tackle the task using a multi-task network, comprising one Change Detection (CD) sub-task and two Semantic Segmentation (SS) sub-tasks. Although these approaches have achieved good performance, one crucial question persists: What is the effective way to handle the feature interactions across SCD sub-tasks? To address this issue, this paper first offers an overview of existing SCD networks and compares them from a perspective view of Multi-Task Learning (MTL). Following that, we select an architecture combining a two-branch encoder and a three-branch decoder as the baseline due to its compatibility with MTL. Then, one simple yet very effective module, decoder feature interaction across sub-tasks (DFIT), is introduced. DFIT seeks to enhance the CD decoding feature by leveraging the feature differences between two SS decoding branches on a layer-wise basis. Additionally, the feature aggregation module (FAM) is designed further to enhance the network performance in cooperation with DFIT. FAM aims to produce more representative shared information across the SS and CD sub-tasks by merging the outputs from the final three encoder layers. Combining DFIT and FAM, the proposed network exploiting Decoder-Focused MTL (DEFO-MTLSCD) presents more representative information by capitalizing on both CD and SS losses back-propagations across all coding paths and achieves better performance. Experimental results reveal that our method outperforms state-of-the-art performances relative to previous SCD efforts. Our source code is released at https://github.com/byyztgxz/Decoder_Fusion.

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recommenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine.

In recent years, Semantic Change Detection (SCD) has emerged as a pivotal field within the Remote Sensing (RS) research community, underscored by its essential contribution to various Earth observation undertakings. Conventional SCD methodologies typically adopt a multi-task network architecture, fusing a Binary Change Detection (BCD) sub-task with dual Semantic Segmentation (SS) sub-tasks. These strategies frequently rely on the encoder’s low-resolution yet semantically dense features, derived from multiple down-sampling stages, as the inputs for the decoding heads. Departing from this traditional path and targeting the nuanced characteristics of the multi-subtasks, this study pioneers a novel methodology that harnesses the potential of the encoding phase’s high-resolution features. By integrating HRNet as the encoder structure, we introduce the BT-HRSCD framework, featuring two simple and effective modules. The first, BiFAM, seeks to imbue features with richer semantic insights through bidirectional feature fusion that spans from shallow-to-deep as well as deep-to-shallow layers. The second module, HRDE, utilizes the encoder’s highest spatial resolution features, evaluating their differences to enhance the precision in identifying change areas. BiFAM is devised to boost the SS sub-tasks’ effectiveness, whereas HRDE aims to elevate the accuracy of the BCD sub-task. Experimental results reveal that our method outperforms state-of-the-art performances relative to previous SCD efforts. Our source code is released at https://github.com/iridescent524/BT-HRSCD.

Person re-identification is an important task in the field of video surveillance that concentrates on identifying the same person across different cameras. Some methods cannot learn effective image representations, due to the low resolution of pedestrian image data sets. In this article, we propose a novel Siamese network architecture with layers specially designed to address the problem of re-identification. The architecture proposed in this work is applied to the edge of the cloud infrastructure, which can accelerate the speed of pedestrian retrieval. Our network outputs a similarity value when a pair of images is given as input, indicating whether the two input images show the same person. Novel elements of our architecture include a residual model layer that includes an “identity block” and a “conv” block, which considerably capture more efficient features between the two input images. A global average pooling layer is adopted to reduce the model complexity before a fully connected layer, which minimizes person retrieval time in edge computing. Our proposed method significantly improves previous on: CUHK03 by 30% in rank-1, Market-1501 by 35% in rank-1. We also demonstrate that the proposed method outperforms most state-of-the-art methods on the two public benchmarks.