RGBT tracking is a task that utilizes images from visible (RGB) and thermal infrared (TIR) modalities to continuously locate a target, which plays an important role in various fields including intelligent transportation systems. Most existing RGBT trackers do not achieve high precision and real-time tracking speed simultaneously. To address this challenge, we propose an innovative RGBT tracker, the Siamese Triple-stream Feature Aggregation Network (SiamTFA). Firstly, a triple-stream backbone is presented to implement multi-modal feature extraction and fusion, which contains two parallel Swin Transformer feature extraction streams, and one feature fusion stream composed of joint-complementary feature aggregation (JCFA) modules. Secondly, our proposed JCFA module utilizes a joint-complementary attention to guide the aggregation of multi-modal features. Specifically, the joint attention can focus on spatial location information and semantic information of the target by combining the features of two modalities. Considering the complementarity between RGB and TIR modalities, the complementary attention is introduced to enhance the information of beneficial modality and suppress the information of ineffective modality. Thirdly, in order to reduce the computational complexity of the joint-complementary attention, we propose a depthwise shared attention structure, which utilizes depthwise convolution and shared features to achieve lightweight attention. Finally, we conduct extensive experiments on four official RGBT test datasets and the experimental results demonstrate that our proposed tracker outperforms some state-of-the-art trackers and the tracking speed reaches 37 frames per second (FPS). The code is available athttps://github.com/zjjqinyu/SiamTFA.

With the extensive deployment of vehicular ad-hoc networks (VANETs), it becomes an inevitable choice to provide enhanced in-vehicle services for uploading a vast amount of shared vehicular data to cloud storage. However, there is still a lack of effective deduplication and audit methods for cloud-stored data in VANET scenarios. To address the securities of cloud-stored data in VANETs, we propose a blockchain-based data deduplication and distributed audit scheme for shared data under cloud-fog computing-based VANETs in this paper. In our scheme, we construct a distributed audit model for VANETs, where road side units (RSUs) are partitioned as multiple management areas. Each management area can solely make their consensus for data integrity verification to audit the cloud storage provider without depending on any third-party auditors (TPAs). Also, we establish a blockchain-based monitoring mechanism maintained by the fog servers to ensure the integrity of the uploading and auditing records and enable related entities within the system to verify corresponding audit results (or records). Furthermore, we propose a lightweight dual-verifier structure to adapt to resource-constrained VANET scenarios. Through our dual-verifier mechanism, our scheme can effectively resist proof-replay attacks. Related theoretical analysis and experimental results show our data deduplication and distributed audit scheme is efficient and effective for VANET scenarios.

The rank- ($L, L, 1,1$) block term decomposition (BTD) with spatial orthonormality (BTD-O) applied to 4-way multi-subject fMRI data achieves good performance due to preserving higher spatial structure and reducing crosstalk between components. However, the high rank$L$value (e.g., 35) of BTD-O for fMRI data leads to high computation complexity. Moreover, multi-subject fMRI data contains high noise nature. Although an accelerated BTD-O (accBTD-O) was proposed, it showed similar performance to BTD-O. Inspired by the compression, smoothing, and spatial structure invariance features of the pooling scheme, we respectively propose weighted spatial 3D and 2D pooling preprocessing for BTD-O of fMRI data. These two methods give higher weight to meaningful in-brain voxels and reduce the size and noise of fMRI data. Specifically, weighted spatial 3D pooling compresses weighted 3D brain images of a 5-way fMRI tensor, then transforms pooled 5-way fMRI tensor into a 4-way fMRI tensor. In contrast, for weighted spatial 2D pooling, the 5-way weighted fMRI data is first transformed into a 4-way tensor, and then 2D brain images of 4-way fMRI tensor are compressed by 2D pooling. The pooled 4-way fMRI tensor is separated by BTD-O to extract shared spatial maps, shared time courses, and subject intensities. Results of simulated and experimental fMRI data analyses both demonstrate that these two proposed methods achieve obviously better task-related component than compared methods. Moreover, the proposed 3D pooling is about 2.243 times faster than accBTD-O.

The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with$l_{2}$-norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an$l_{1}$-norm spatial regularization term. This converts the original ridge regression equation into an Elastic Net regression, which allows the filter to have a certain sparsity while maintaining the stability of model optimization. The entire AS2RCF model is optimized using alternating direction method of multipliers(ADMM), and quantitative evaluations through extensive experiments on OTB-2015, TC128 and UAV123 demonstrate the tracker's effectiveness.

The decomposition of multi-subject fMRI data using rank-(L,L,1,1) block term decomposition (BTD) can preserve higher-way data structure and is more robust to noise effects by decomposing shared spatial maps (SMs) into a product of two rank-L loading matrices. However, since the number of whole-brain voxels is very large and rank L is larger than 1, the rank-(L,L,1,1) BTD requires high computation and memory. Therefore, we propose an accelerated rank-(L,L,1,1) BTD algorithm based upon the method of alternating least squares (ALS). We speed up updates of loading matrices by reducing fMRI data into subspaces, and add an orthonormality constraint on shared SMs to improve the performance. Moreover, we evaluate the rank-L effect on the proposed method for actual task-related fMRI data. The proposed method shows better performance when L=35. Meanwhile, experimental comparison results verify that the proposed method largely reduced (17.36 times) computation time compared to ALS while also providing satisfying separation performance.

Abstract Deep learning approaches to interactive image segmentation are typically formulated as a binary labeling problem. A model trained to make predictions within a fixed set of labels (i.e., foreground and background labels) cannot be used to directly predict the binary masks of multiple objects of interest, which greatly limits its flexibility and adaptivity. The use of different classes of clicks as input is opted for and the first end‐to‐end learning model for multi‐object segmentation, based on a new designed neural network, is developed. The network consists of a visual feature extractor, a recurrent attention module and a dynamic segmentation head, extracts user click‐adapted appearance embedding features and spatial attention features, and then learns to transform this information into a segmentation of multiple objects. It is also proposed to train the network using a joint loss function, taking the embedding learning into account for segmentation. Comprehensive experiments are conducted on three benchmark datasets to demonstrate the effectiveness of the proposed method. It performs favorably against state‐of‐the‐art approaches on the multiple object segmentation task, for example, with 0.15 s per image, 0.06 s per object and mean IoU & F1 score of 84.90% on Pascal VOC 2012 validation set. It is further shown that the method can be used in numerous vision applications such as image recoloring and colorization.

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network-based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full-image removal of blocking artifacts. Specifically, with our multi-channel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block-based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available athttps://github.com/siwangzhou/DeepBCS.

Multi-proxy signature is a variant of proxy signature, which allows that a delegator (original signer) may delegate his signing rights to many proxy signers. Comparing with proxy signatures, multi-proxy signatures can effectively prevent that some of proxy signers abuse signing rights. Also, with the rapid development of identity-based cryptography, identity-based multi-proxy signature (IBMPS) schemes have been proposed. Comparing with proxy signature based on public key cryptography, IBMPS can simplify key management and be used for more applications. Presently, many identity-based multi-proxy signature schemes have been proposed, but most of them are constructed in the random oracle model. Also, the existing security model for identity-based multi-proxy signature is not enough complete according to the Boldyreva et al.’s work. In this paper, we present a framework for IBMPS on n + 1 users ( n is the number of proxy signers participating in signing), and show a detailed security model for IBMPS. Under our framework, we present an identity-based multi-proxy signature scheme in the standard model. Comparing with other identity-based multi-proxy signature schemes, the proposed scheme has more complete security.