The fusion module of RGB and infrared (IR) remote sensing images is the key of multispectral ship detection. Existing works have shown that the cross-attention-based feature fusion can achieve good performance by extracting the complementary information of RGB and IR modalities. However, the existing commonly used cross-attention mechanisms introduce lots of redundancy parameters and mainly focus on global feature interaction of multispectral images, ignoring local detail information that is also important for small ship detection. In this paper, we propose a novel multispectral ship detection approach named LoGFusion. In LoGFusion, we design the cross stage partial module with partial convolution (CSPMPC) to reduce feature redundancy and utilize the local cross-modal fusion module (LoCFM) and global cross-modal fusion module (GCFM) to capture both local and global cross-modal features. Furthermore, we introduce a Multispectral Small Ship Dataset (MSSD) containing over 5k ship targets for small target detection. Experiments on MSSD validate the effectiveness of our method in terms of small ship detection in multispectral images.

In recent years, hypergraph analysis have attracted increasing attention due to their ability to model complex data correlation, with hypergraph clustering being one of the most important tasks. However, when the scale of hypergraph is large enough, clustering is difficult based on global consistency. Existing flow-based hypergraph local clustering methods have good theoretical cut improvements and runtime guarantees. However, these methods exhibit poor performance when the initial reference node set is small and are prone to causing the output set to shrink into a small subset, resulting in local minima. To address this issue, we propose the Penalized Flow Hypergraph Local Clustering(PFHLC) and provide new conductance guarantees and runtime analyses for our method. First, we use the random walk method to grow the initial seed set, and introduce the random walk information of nodes as penalized flow into the flow-based framework to optimize the output. Second, we propose a generalized objective function containing random walk information, which takes full advantage of the semi-supervised information of the target cluster to protect important nodes. This feature can avoid the local minima of previous flow-based methods. Importantly, our method is strongly-local and can run efficiently on large-scale hypergraphs. We contribute a real-world dataset and the experiments on real-world large-scale datasets show that PFHLC achieves the state-of-the-art significantly.

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer prediction, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental resu- lts show that our network achieves significant performance against the previous state-of-the-art methods.

In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distillation approach to facilitate personalized federated learning. In this approach, each client trains its local model and only sends the backbone weights to the server. These weights are then aggregated to create a global backbone, which is returned to each client for updating. However, the client’s local backbone lacks personalization because of the common representation. To solve this problem, each client further performs backbone self-distillation by using the global backbone as a teacher and transferring knowledge to update the local backbone. This process involves learning two components: the shared backbone for common representation and the private head for local personalization, which enables effective global knowledge transfer. Extensive experiments and comparisons with 12 state-of-the-art approaches demonstrate the effectiveness of our approach.

The embedded representation and clustering tasks both play important roles in relational data analysis and mining. Traditional methods mainly employ graph structure to describe relational data, but intuitive pairwise connections among nodes are insufficient to model high-order data in the real-world, such as the relations between proteins and polypeptide chains. Hypergraphs are a generalization of graphs, and hypergraphs can well model high-order data. When modeling relational data in the real world, hypergraphs are often accompanied by node attributes, i.e. attributed hypergraphs. Besides this, how to integrate the structural information and attribute information appropriately is another important task, while has not been investigated systematically. In this paper, we propose Adaptive Hypergraph Auto-Encoder(AHGAE) to learn node embeddings in low-dimensional space. Our method can utilize the high-order relation to generate embedding for clustering. It is composed of two procedures, i.e. the adaptive hypergraph Laplacian smoothing filter and the relational reconstruction auto-encoder. It has the advantage of integrating more complex data relations compared with graph-based methods, which leads to better modeling and clustering performance. The proposed method has been evaluated on hypergraph datasets and benchmark graph datasets. Experimental results and comparison with the state-of-the-art methods have demonstrated the effectiveness of our proposed method.

Change captioning is an emerging task to describe the changes between a pair of images. The difficulty in this task is to discover the differences between the two images. Recently, some methods have been proposed to address this problem. However, they all employ unidirectional difference localization to identify the changes. This can lead to ambiguity about the nature of the changes. Instead, we propose a framework with bidirectional difference localization and semantic consistency reasoning to describe the image changes. First, we locate the changes in the two images by capturing bidirectional differences. Then we design a decoder with spatial-channel attention to generate the change caption. Finally, we introduce semantic consistency reasoning to constrain our bidirectional difference localization module and spatial-channel attention module. Extensive experiments on three public data sets show that the performance of our proposed model outperforms the state-of-the-art change captioning models by a large margin.

By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.

As the next generation standard of video coding, High Efficiency Video Coding (HEVC) is expected to be more complex than H.264/AVC. Many-core platforms are good candidates for speeding up HEVC in the case that HEVC can provide sufficient parallelism. The local parallel method (LPM) is the most promising parallel proposal for HEVC motion estimation (ME), but it can't provide sufficient parallelism for many-core platforms. On the premise of keeping the data dependencies and coding efficiency the same as the LPM, we propose a highly parallel framework to exploit the implicit parallelism. Compared with the well-known LPM, experiments conducted on a 64-core system show that our proposed method achieves averagely more than 10 and 13 times speedup for 1920×1080 and 2560×1600 video sequences, respectively.

Summary form only given. Many-core platforms are good candidates for speeding up High Efficiency Video Coding (HEVC) in the case that HEVC can provide sufficient parallelism. As the most promising proposal for parallelizing HEVC deblocking filter (DF), the order-changed parallel method (OCPM) changes the order of filtering and incurs considerable loss in coding efficiency. Meanwhile, the parallelism of OCPM still has some room for improvement. In this paper, we propose an efficient parallel framework for HEVC DF, which exploits the implicit parallelism and keeps the filtering order of DF unchanged. Compared with the well-known OCPM, experiments conducted on a 64-core system show that our proposed method saves averagely 37.18% and 37.93% DF time with different quantization parameters (QPs). Meanwhile, our proposed method improves coding efficiency, which achieves an average BD-rate reduction of 0.09%, 0.11% and 0.12% for Y, U and V components, respectively.