This paper investigates the fixed/prescribed-time synchronization problem for fuzzy inertial memristive neural networks (FIMNNs) with hybrid delays. Within a unified framework, a comparative study was conducted on the interval matrix method and the maximum absolute value method with respect to the state-dependent switching parameters induced by memristive characteristics. Correspondingly, two different controllers are designed, with theorem constraints expressed algebraically and as LMIs. Numerical experiments demonstrate that, under identical initial system parameters, the interval matrix method constructs a Lyapunov–Krasovskii functional (LKF) incorporating an integral term, enabling finer handling of time-delay effects. As a result, it provides a more accurate estimate of the upper bound of the settling time (ST) compared to the maximum absolute value method, thereby achieving faster and more efficient synchronization control. Ultimately, the proposed results are applied to image encryption, thereby demonstrating its theoretical significance and practical utility.

Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD). Existing studies on CAOD predominantly rely on RGB cameras, but these frame-based sensors usually have high latency and limited dynamic range, leading to safety risks under extreme conditions like fast-moving objects, overexposure, and darkness. In this study, we turn to the event-based vision, featured by its sub-millisecond latency and high dynamic range, for robust CAOD. We propose Detecting Every Object in Events (DEOE), an approach aimed at achieving high-speed, class-agnostic object detection in event-based vision. Built upon the fast event-based backbone: recurrent vision transformer, we jointly consider the spatial and temporal consistencies to identify potential objects. The discovered potential objects are assimilated as soft positive samples to avoid being suppressed as backgrounds. Moreover, we introduce a disentangled objectness head to separate the foreground-background classification and novel object discovery tasks, enhancing the model's generalization in localizing novel objects while maintaining a strong ability to filter out the background. Extensive experiments confirm the superiority of our proposed DEOE in both open-set and closed-set settings, outperforming strong baseline methods.

Recent advances in deep learning-based methods have led to significant progress in the hyperspectral super-resolution (SR). However, the scarcity and the high dimension of data have hindered further development since deep models require sufficient data to learn stable patterns. Moreover, the huge domain differences between hyperspectral image (HSI) datasets pose a significant challenge in generalizability. To address these problems, we present a general hyperspectral SR framework via meta-transfer learning (MTL). We randomly sample various spectral ranges for SR tasks during MTL, allowing the model to accumulate diverse task experiences. Additionally, we implement a task schedule to gradually expand the number of bands, bridging the significant domain differences between datasets. By leveraging multiple datasets, we are able to achieve better performance and greater generalizability, making it applicable under various circumstances. Meanwhile, as a general framework, our scheme can be applied to existing methods to obtain performance improvements. In addition, we design an advanced network architecture based on the multifusion features to further improve the performance. Experiments demonstrate that our method not only achieves superior performance in both qualitative and quantitative terms but also can adapt robustly to a new and difficult sample, where few epochs can yield quite considerable results.

In the realm of AI, data serves as a pivotal resource. Real-world hyperspectral images (HSIs), bearing wide spectral characteristics, are particularly valuable. However, the acquisition of HSIs is always costly and time-intensive, resulting in a severe data-thirsty issue in HSI research and applications. Current solutions have not been able to generate a sufficient volume of diverse and reliable synthetic HSIs. To this end, our study formulates a novel, generalized paradigm for HSI synthesis, i.e., unmixing before fusion, that initiates with unmixing across multi-source data and follows by fusion-based synthesis. By integrating unmixing, this work maps unpaired HSI and RGB data to a low-dimensional abundance space, greatly alleviating the difficulty of generating high-dimensional samples. Moreover, incorporating abundances inferred from unpaired RGB images into generative models allows for cost-effective supplementation of various realistic spatial distributions in abundance synthesis. Our proposed paradigm can be instrumental with a series of deep generative models, filling a significant gap in the field and enabling the generation of vast high-quality HSI samples for large-scale downstream tasks. Extension experiments on downstream tasks demonstrate the effectiveness of synthesized HSIs. The code is available at HSI-Synthesis.github.io.

In recent years, many single hyperspectral image super-resolution methods have emerged to enhance the spatial resolution of hyperspectral images without hardware modification. However, existing methods typically face two significant challenges. First, they struggle to handle the high-dimensional nature of hyperspectral data, which often results in high computational complexity and inefficient information utilization. Second, they have not fully leveraged the abundant spectral information in hyperspectral images. To address these challenges, we propose a novel hyperspectral super-resolution network named SNLSR, which transfers the super-resolution problem into the abundance domain. Our SNLSR leverages a spatial preserve decomposition network to estimate the abundance representations of the input hyperspectral image. Notably, the network acknowledges and utilizes the commonly overlooked spatial correlations of hyperspectral images, leading to better reconstruction performance. Then, the estimated low-resolution abundance is super-resolved through a spatial spectral attention network, where the informative features from both spatial and spectral domains are fully exploited. Considering that the hyperspectral image is highly spectrally correlated, we customize a spectral-wise non-local attention module to mine similar pixels along spectral dimension for high-frequency detail recovery. Extensive experiments demonstrate the superiority of our method over other state-of-the-art methods both visually and metrically. Our code is publicly available at https://github.com/HuQ1an/SNLSR.

Deep Neural Network (DNN) models have offered powerful solutions for a wide range of tasks, but the cost to develop such models is nontrivial, which calls for effective model protection. Although black-box distribution can mitigate some threats, model functionality can still be stolen via black-box surrogate attacks. Recent studies have shown that surrogate attacks can be launched in several ways, while the existing defense methods commonly assume attackers with insufficient in-distribution (ID) data and restricted attacking strategies. In this paper, we relax these constraints and assume a practical threat model in which the adversary not only has sufficient ID data and query times but also can adjust the surrogate training data labeled by the victim model. Then, we propose a two-step categorical inference poisoning (CIP) framework, featuring both poisoning for performance degradation (PPD) and poisoning for backdooring (PBD). In the first poisoning step, incoming queries are classified into ID and (out-of-distribution) OOD ones using an energy score (ES) based OOD detector, and the latter are further classified into high ES and low ES ones, which are subsequently passed to a strong and a weak PPD process, respectively. In the second poisoning step, difficult ID queries are detected by a proposed reliability score (RS) measurement and are passed to PBD. In doing so, the first step OOD poisoning leads to substantial performance degradation in surrogate models, the second step ID poisoning further embeds backdoors in them, while both can preserve model fidelity. Extensive experiments confirm that CIP can not only achieve promising performance against state-of-the-art black-box surrogate attacks like KnockoffNets and data-free model extraction (DFME) but also work well against stronger attacks with sufficient ID and deceptive data, better than the existing dynamic adversarial watermarking (DAWN) and deceptive perturbation defense methods. PyTorch code is available athttps://github.com/Hatins/CIP_master.git.

Although natural image super-resolution methods have achieved impressive performance, single hyperspectral image super-resolution still remains a challenge due to the high dimensionality. In recent years, many single hyperspectral image super-resolution methods adopted the group-convolution strategy to design the network for reducing the computational burden. However, these methods still process all spectral bands at once during the deep feature extraction and reconstruction, which increases the difficulty of fully exploring the inherent data characteristic of hyperspectral images. Moreover, the advanced group-based methods make insufficient exploitation of complementary information contained in different bands, resulting in limited reconstruction performance. In this paper, we propose a novel group-based single hyperspectral image super-resolution method termed GELIN to reconstruct high-resolution images in a group-by-group manner, which alleviates the difficulty of feature extraction and reconstruction for hyperspectral images. Specifically, a spatial-spectral embedding learning module is designed to extract rewarding spatial details and explore the correlations among spectra simultaneously. Considering the high similarity among different bands, a neighboring group integration module is proposed to fully exploit the complementary information contained in neighboring image groups to recover missing details in the target image group. Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.

Hyperspectral images with tens to hundreds of spectral bands usually suffer from low spatial resolution due to the limitation of the amount of incident energy. Without auxiliary images, the single hyperspectral image super-resolution (SR) method is still a challenging problem because of the high-dimensionality characteristic and special spectral patterns of hyperspectral images. Failing to thoroughly explore the coherence among hyperspectral bands and preserve the spatial–spectral structure of the scene, the performance of existing methods is still limited. In this article, we propose a novel single hyperspectral image SR method termed RFSR, which models the spectrum correlations from a sequence perspective. Specifically, we introduce a recurrent feedback network to fully exploit the complementary and consecutive information among the spectra of the hyperspectral data. With the group strategy, each grouping band is first super-resolved by exploring the consecutive information among groups via feedback embedding. For better preservation of the spatial–spectral structure among hyperspectral data, a regularization network is subsequently appended to enforce spatial–spectral correlations over the intermediate estimation. Experimental results on both natural and remote sensing hyperspectral images demonstrate the advantage of our approach over the state-of-the-art methods.

Most existing light field (LF) super-resolution (SR) methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views. To address these issues, we propose a novel integration network based on macro-pixel representation for the LF SR task, named MPIN. Restoring the entire LF image simultaneously, we couple the spatial and angular information by rearranging the four-dimensional LF image into a two-dimensional macro-pixel image. Then, two special convolutions are deployed to extract spatial and angular information, separately. To fully exploit spatial-angular correlations, the integration resblock is designed to merge the two kinds of information for mutual guidance, allowing our method to be angular-coherent. Under the macro-pixel representation, an angular shuffle layer is tailored to improve the spatial resolution of the macro-pixel image, which can effectively avoid aliasing. Extensive experiments on both synthetic and real-world LF datasets demonstrate that our method can achieve better performance than the state-of-the-art methods qualitatively and quantitatively. Moreover, the proposed method has an advantage in preserving the inherent epipolar structures of LF images with a balanced distribution of performance.

Deep convolutional neural networks (CNNs) have recently made a considerable achievement in the single-image super-resolution (SISR) problem. Most CNN architectures for SIS-R incorporate skip connections to integrate features, and treat them equally. However, this neglects the discrimination of features, and consequently, achieving relatively poor performance. To address this problem, we introduce a deep aggregation network that merging extraction and aggregation nodes in a tree structure, which can aggregate features progressively. In particular, we rescale the information in the aggregation node by modelling the interaction between channels, which shares the same insight on the attention mechanism for improving the discriminative ability of network. In the extraction node, we introduce an mlpconv layer into a dense unit that is parallel to the convolutional layer and can improve the nonlinear mapping capability, where the residual learning is utilized to accelerate the training process. Extensive experiments conducted on several publicly available datasets have demonstrated the superiority of our model over state-of-the-art in objective metrics and visual impressions.