Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signal-to- noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global self-attention. This paper presents a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components designed to enhance both global context modeling and local structure preservation: Locally-enhanced Context Encoding (LeCE) for improving local structural awareness and enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (AL-SWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes in local context. Comprehensive experiments across three large-scale outdoor sparse LiDAR datasets, including SemanticKITTI, nuScenes, and LiDAR-CS, as well as two indoor dense human body datasets, including 8iVFB and MVUB, and one indoor dense scenario dataset, ScanNet, demonstrate that our TopNet achieves state-of-the-art compression performance with fewer parameters.

Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signalto-noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global selfattention. In this paper, we propose a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components: Locally-enhanced Context Encoding (LeCE) for enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (ALSWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes. Comprehensive experiments across both indoor and outdoor point cloud datasets demonstrate that our TopNet achieves state-ofthe-art performance with fewer parameters, further advancing the reduction-efficiency boundaries of PCGC. The code is available at https://github.com/xinjiewang1995/TopNet.

Infrared small target detection is of great significance for various applications. However, it is significantly challenged by complex backgrounds and low signal-to-clutter ratio. Although low-rank and sparse decomposition (LRSD)-based methods are widely employed, they are hampered by fixed temporal step sizes, transpose errors in tensor recovery, and the suboptimal approximation of sparsity using thel1norm. To tackle these problems, we propose an entropy-based adaptive spatial-temporal infrared tensor with nonconvex weighted average tensor rank (EASTIT-NWTAR) method. Firstly, we propose an adaptive spatial-temporal tensor construction approach that leverages tensor information entropy to dynamically adjust the temporal step size, ensuring an accurate representation of background changes and maintaining its low-rank property. Secondly, we propose a nonconvex weighted tensor norm combining the Laplace norm and weighted average tensor rank (WTAR) norm to effectively mitigate transpose errors and enhance low-rank recovery. Finally, we substitute thel1norm with the smoothly clipped absolute deviation (SCAD) norm to improve sparse target reconstruction accuracy. The proposed method is effectively solved using the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that proposed method outperforms state-of-the-art methods in both target detection and background suppression.

Satellite videos have played important roles in many applications in recent years due to the advantages of continuous providing high temporal resolution remote sensing images. Although much progress has been achieved for moving object detection (MOD) in satellite videos, the low-rank characteristics of background and the intensity variations of moving objects across frames have not been fully exploited. In this article, we propose an efficient method for MOD in satellite videos, which models the background with enhanced 3-D total variation (E-3DTV) regularization and the moving objects with Gaussian prior. Specifically, considering that the gradient maps on the spatial and temporal dimensions exhibit different physical meanings, we model the background with different Laplacian sparsity priors for the gradient maps along the spatial and temporal dimensions for 3DTV regularization. Different from current methods, which model moving objects with sparsity characteristics in each frame alone, we utilize Gaussian prior to model intensity changing characteristics of moving objects across frames. After integrating background model and moving object model into low-rank sparse matrix factorization framework, the alternating direction method of multipliers (ADMM) is adopted to iteratively optimize the parameters of background and moving object models. We conduct experiments on VISO and SkySat datasets, and the results demonstrate that our method achieves superior MOD performance with high computational efficiency compared to state-of-the-art methods.

In the context of escalating frequency spectrum congestion, the prevalence of radio frequency interference (RFI) poses a growing challenge for weather radars, compromising data quality and adversely affecting the accuracy of variable estimations. This article proposes a novel algorithm for the separation of precipitation and RFI, based on low-rank sparse decomposition (LRSD). When precipitation and RFI overlap, this method effectively filters out RFI while minimizing its impact on precipitation by analyzing the property difference between precipitation and RFI in the time domain. The proposed algorithm operates on the assumption that precipitation exhibits low-rank properties, whereas RFI manifests as sparse signals. This assumption is grounded in the observation that RFI in weather radars is typically unintentional, occupying a limited number of pixels in the range-time power image, while precipitation demonstrates approximate stationarity with a slow speed within a coherent processing interval (CPI). This algorithm is intended to alleviate the adverse effects of RFI, and the efficacy of the approach is validated using data collected by polarimetric Doppler weather radar systems at the Royal Netherlands Meteorological Institute. This article systematically evaluates the impact of the proposed LRSD method in comparison to two traditional RFI mitigation strategies (e.g., the Vaisala-3 method and 2-D filter) on the quality of meteorological data. The results demonstrate that the proposed LRSD method outperforms the alternatives in terms of RFI suppression and precipitation retention performance. At present, the LRSD method loses phase information, which may impact the accuracy of measuring polarization parameters of precipitation.

Weakly supervised semantic segmentation(WSSS) with image-level labels has witnessed promising advances with the help ofclass activation maps(CAM). However, CAM is always confined to small discriminative seed regions due to its simple classification loss guided training manner. To handle this problem, recent works introduced specifically designed regularizations and modules to expand the CAM seed regions, serving as the final segmentation masks. In this paper, we surprisingly find that the classification loss could suppress the gains from these regularization and modules in the late training phase, thereby limiting the further growth of CAM, which we call as theexplicit supervision disturb(ESD) issue. Interestingly, we find that specificdata augmentation(DA) operations (e.g., CutMix) can relieve such ESD issue, and the benefits introduced by different DA operations vary a lot. To maximize the benefits, we proposedifferentiable data augmentation(DDAug) to automatically search for the proper DA policy. Specifically, we design amulti-level search spaceto sequentially sample DA operations with different properties. Extensive experiments demonstrate that the proposed DDAug can alleviate the ESD issue and introduce consistent improvements to various popular WSSS methods, achieving the state-of-the-art performance on the MS COCO 2014 and PASCAL VOC 2012 datasets.

Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data with expensive pixel-level annotations. To reduce the annotation burden, we propose the first method to achieve SIRST detection with single-point supervision. The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters. To handle this issue, we introduce randomness to the clustering process by adding noise to the input images, and then obtain much more reliable pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo" clustering approach, our method can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation. Experiments on four datasets demonstrate that our method can be applied to existing SIRST detection networks to achieve comparable performance with their fully-supervised counterparts, which reveals that single-point supervision is strong enough for SIRST detection. Our code will be available at: https://github.com/YeRen123455/SIRST-Single-Point-Supervision.

Many existing low-rank methods have achieved good detection performance in uniform scenes, but they suffer from a high false alarm rate in complex noisy scenes. Therefore, it is important to improve the detection performance of low-rank models in noisy scenes. In this paper, we first formulate an implicit regularizer by plugging a denoising neural network (termed as deep denoiser), which can learn deep image priors from a large number of natural images. Then, we use the weighted sum of weighted tensor nuclear norm for more accurate background estimation. Finally, alternating direction multiplier method is used to solve the model under the plug-and-play framework. By integrating low-rank prior with deep denoiser prior, our model achieves higher accuracy. Experiments on different scenes demonstrate that our method achieves an improved performance in terms of visual effects and quantitative metrics. Specially, the overall accuracy of AUC value (AUCOA) achieved by the proposed method on Sequences 1-6 are 1.24%, 1.16%, 0.63%, 1.9%, 0.82%, 2.06% higher than those achieved by the second top performing methods, respectively.

Infrared small target detection in complex scenes is an important but challenging research hotspot in infrared early warning fields. Previous studies have proved that low-rank Tucker decomposition (TD) achieves good detection performance in complex scenes. However, a key limitation of existing low-rank TD methods is that the rank needs to be set in advance, and an inaccurate predefined rank can lead to performance degradation. Inspired by the theorem that n-rank is upper bounded by the rank of each Tucker factor matrix, we propose a nonconvex tensor TD model with factor prior for infrared small target detection. In our method, we use a logdet-based function to constrain the latent factors of low-rank TD, which avoids empirical rank selection and sufficiently uses the latent data structure information in the factor matrix. Meanwhile, performing singular value decomposition (SVD) calculations on small factor matrices can reduce computational complexity. Then, group sparsity regularized total variation is used to better exploit the shared sparse pattern of difference images, which helps better remove background clutter and obtain better detection results. Finally, the proposed method is efficiently solved by the well-designed alternating direction method of multipliers (ADMM). Extensive experimental results demonstrate that our method is more effective and robust in complex scenes than other state-of-the-art methods.

Low-rank and sparse decomposition based models are powerful and robust tools for infrared small target detection. However, due to the calculation of singular value decomposition (SVD) and the optimization of complex regularization terms, existing low-rank models often suffer from high computational complexity. To solve this problem, based on the theorem that representative coefficient matrix obtained by orthogonal transformation of data matrix can inherit the spatial structure of data matrix, we propose a representative coefficient total variation (RCTV) method for efficient infrared small target detection. In our method, we use total variational to constraint representative coefficient matrix instead of data matrix to describe local smooth prior, which helps remove noise and reduce computational complexity. Meanwhile, we control the number of columns in the representative coefficient matrix to maintain the low-rank characteristics of background, which avoids SVD calculation and improves detection efficiency. Therefore, the RCTV regularization can simultaneously describe local smooth prior and low-rank prior. Moreover, to better enhance the sparsity of targets and distinguish sparse non-target points, we use the log-sum function to adaptively assign weights to targets. It helps obtain more accurate detection performance. The proposed model is efficiently solved by the alternating direction multiplier method (ADMM). A large number of experiments show that the proposed method is superior to existing low-rank methods in both detection accuracy and efficiency.

Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by Earth-orbiting satellites. Due to the extremely large image coverage area (e.g., thousands of square kilometers), candidate targets in these images are much smaller, dimer, and more changeable than those targets observed by aerial- and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and$17\,598$pixel-level tiny ship annotations. Each image covers about$10\,000$km2of area with$10 \ 000\,\, \times \ 10 \ 000$pixels. Considering the extreme characteristics (e.g., small, dim, and changeable) of those tiny ships in such challenging scenes, we propose a multilevel TransUNet (MTU-Net) in this article. Specifically, we design a vision Transformer (ViT) convolutional neural network (CNN) hybrid encoder to extract multilevel features. Local feature maps are first extracted by several convolution layers and then fed into the multilevel feature extraction module [multilevel ViT module (MVTM)] to capture long-distance dependency. We further propose a copy–rotate–resize–paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning-based single-frame infrared small target (SIRST) methods in terms of probability of detection, false alarm rate, and intersection over union. Our code is available athttps://github.com/TianhaoWu16/Multi-level-TransUNet-for-Space-based-Infrared-Tiny-ship-Detection

Background reconstruction is a key step of moving object detection in satellite videos. Most existing model-based methods exploit low-rank prior to recover background, which have achieved good performance but suffered degradation under complex and dynamic scenes. In this paper, we introduce a deep background prior into model-based methods for moving vehicle detection in satellite videos. Our deep background prior is obtained by a background reconstruction network, which can learn to reconstruct background from consecutive frames. By applying our deep background prior into model-based methods, a closed-form solution can be obtained via alternating direction method of multipliers (ADMM) and then detection results can be acquired through iterative optimization. More importantly, our background reconstruction network can be trained in an unsupervised way by introducing specifically designed loss, thus relieving the dependence on large-scale labeled dataset. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed method.

Low-rank matrix decomposition approaches have achieved significant progress in small and dim object detection in satellite videos. However, it is still challenging to achieve robust performance and fast processing under complex and highly heterogeneous backgrounds since satellite video data can neither adequately fit the foreground structure nor the background model in the existing matrix decomposition models. In this letter, we propose a novel object detection method based on a spatial–temporal tensor data structure. First, we construct a tensor data structure to exploit the inner spatial and temporal correlation within a satellite video. Second, we extend the decomposition formulation with bounded noise to achieve robust performance under complex backgrounds. This formulation integrates low-rank background, structured sparse foreground, and their noises into a tensor decomposition problem. For background separation, a weighted Schatten$p$-norm is incorporated to provide adaptive threshold to obtain the singular value of the background tensor. Finally, the proposed model is solved using the alternative direction method of multipliers (ADMM) scheme. Experimental results on various real scenes demonstrate the superiority of the proposed method against the compared approaches.

Infrared small target detection is an important fundamental task in the infrared system. Therefore, many infrared small target detection methods have been proposed, in which the low-rank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this article, we propose a nonconvex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA regularization adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we propose asymmetric spatial–temporal total variation (ASTTV) regularization to achieve more accurate background estimation in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness intensities for spatial and temporal regularization. We design an efficient algorithm to find the optimal solution for our method. Compared with some state-of-the-art methods, the proposed method achieves an improvement in terms of various evaluation metrics. Extensive experimental results in various complex scenes demonstrate that our method has strong robustness and a low false-alarm rate.