Object detection generally involves two main components: classification and regression. Despite the impressive performance achieved by recent refinement localization works, there is still room for improvement due to the limitations of current multistep regression strategies and task misalignment. To overcome these challenges, we propose a novel offset-aware progressive regression detector (OAPR) comprising an offset-aware head and a progressive regression predictor. Initially, we develop a head network incorporating our innovative plug-and-play offset-aware module. By utilizing the offset from one task to guide feature learning in another task, we intuitively achieve task alignment to address feature misalignment. We subsequently employ a progressive regression predictor to locate objects. In first-step regression, the aim is to identify a region within the object rather than the object itself. This is followed by second-step regression to locate the object precisely. Extensive experiments conducted on MS COCO datasets demonstrate the superior performance of our OAPR compared with recent state-of-the-art detectors with various backbones, including ATSS (~3.0 AP), GFL (~2.0 AP), BorderDet (~2.0 AP), and VFNet (~1.0 AP). Our code will be released.

Efficient extraction of spectral sequences and geospatial information is crucial in hyperspectral image (HSI) classification. Recurrent neural networks (RNNs) and Transformers excel in capturing long-range spectral features, while convolutional neural networks (CNNs) excel in aggregating spatial information through convolutional kernels. However, RNNs and Transformers suffer from low-computational efficiency, and CNNs have limitations in perceiving global contextual information. To address these issues, this article proposes GraphMamba—an efficient graph structure learning vision Mamba for HSI classification. Specifically, GraphMamba is a novel hyperspectral information processing paradigm that preserves spatial-spectral features by constructing spatial-spectral cubes and employs a linear spectral encoder to enhance the operability of subsequent tasks. The core components of GraphMamba include the HyperMamba module, which enhances computational efficiency, and the SpatialGCN module, designed for adaptive spatial context awareness. The HyperMamba mitigates clutter interference by employing a global mask (GM) and introduces a parallel training and inference architecture to alleviate computational bottlenecks. Meanwhile, the SpatialGCN utilizes weighted multihop aggregation (WMA) for spatial encoding, emphasizing highly correlated spatial structural features. This approach enables flexible aggregation of contextual information while minimizing spatial noise interference. Notably, the encoding modules of the proposed GraphMamba architecture are both flexible and scalable, providing a novel approach for the joint mining of spatial-spectral information in hyperspectral images. Extensive experiments were conducted on three different scales of real HSI datasets. When compared with state-of-the-art classification methods, GraphMamba demonstrated superior performance. The core code will be released athttps://github.com/ahappyyang/GraphMamba.

Due to the scarcity of prior information and the high complexity of spectral data, hyperspectral image (HSI) clustering presents a significant challenge. Although recent deep clustering methods have demonstrated remarkable performance, their intricate network structures and poor robustness hinder their practical application. To address this issue, we propose an efficient and lightweight spectral-spatial feature graph contrastive learning (S2GCL) framework for robust HSI clustering. Specifically, we have designed a novel spectral-spatial feature encoder that fully leverages the information in HSI by incorporating both spatial structure and spectral similarity matrices. To establish a lightweight model, we implement several effective designs: First, S2GCL eliminates the commonly used data augmentation and discriminator in GCL during the generation of positive embeddings. Second, we use a multilayer perceptron (MLP) to produce low-dimensional embeddings instead of relying on graph convolutional networks (GCNs). Third, negative embeddings are generated through row-shuffling, avoiding the use of neural networks. Finally, we propose a multiple boundary loss function to extract complementary information from spatial structures and neighboring nodes, while also constraining the interclass differences between positive and negative examples. We conducted extensive experiments on four publicly available datasets and compared S2GCL with state-of-the-art clustering methods. The results indicate that S2GCL achieves satisfactory performance. The code for S2GCL will be released athttps://github.com/ahappyyang/S2GCL.

Anchor-based detectors have been continuously developed for object detection. However, the individual anchor box makes it difficult to predict the boundary’s offset accurately. Instead of taking each bounding box as a closed individual, we consider using multiple boxes together to get prediction boxes. To this end, this paper proposes the Box Decouple-Couple(BDC) strategy in the inference, which no longer discards the overlapping boxes, but decouples the corner points of these boxes. Then, according to each corner’s score, we couple the corner points to select the most accurate corner pairs. To meet the BDC strategy, a simple but novel model is designed named the Anchor-Intermediate Detector(AID), which contains two head networks, i.e., an anchor-based head and an anchor-free Corner-aware head. The corner-aware head is able to score the corners of each bounding box to facilitate the coupling between corner points. Extensive experiments on MS COCO show that the proposed anchor-intermediate detector respectively outperforms their baseline RetinaNet and GFL method by ∼2.4 and ∼1.2 AP on the MS COCO test-dev dataset without any bells and whistles.

Abstract Convolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential ability of frequency domain feature extraction. In this study, the mechanism in the backbone design is explored. Firstly, the traditional DCT formula is converted into a convolution form through mathematical derivation. On the basis of a a new type of convolution, namely the DCT Convolution is designed. It is more applicable to deep learning network architectures. Secondly, based on the DCT Convolution, a new cross‐domain fusion network named CDF‐Net is designed. The frequency domain and spatial domain features of the input sample are extracted and fused by the network. CDF‐Net is a general network framework which can be applied to most existing prevalent networks. Finally, various experiments are conducted. On image classification task, for Imagenet2012 dataset, the method proposed was applied to ResNet50, and the accuracy of Top1 was increased by 3.684%. On object detection task, for COCO2017 dataset, the method proposed in this study was applied to ResNet50 and ResNeXt50, mAP were improved by 0.5% and 1.2% respectively.

Existing ship detection approaches in SAR images often suffer from inadequate learning of the detector and sub-optimal detection performance. To this end, Based on self-supervised contrastive learning, this letter consider using the relationship between samples to develop a more effective training strategy. First, the Instance-based RoI encode head is proposed, named InsRen head, a simple yet effective network structure. Its purpose is to encode the samples into a contrastive feature space, facilitating the measurement of contrastive learning. Furthermore, to adapt contrastive learning to ship detection, we have redefined some basic terms, such as query, positive key, and negative key, which can help the model build the training pipeline. Finally, we design the Instance-based Contrastive loss that does not require label supervision, named InsCon loss. With the penalty of the InsCon loss, the queries and positive key can learn more similar representations in the contrastive feature space. Simultaneously, the query and negative key are as far away as possible to increase the difference. With the help of InsRen head and InsCon loss, the training of the detection model is more effective. Experimental results demonstrate the superiority of our method.

Transformer has been widely used in classification tasks for hyperspectral images (HSI) in recent years. Because it can mine spectral sequence information to establish long-range dependence, its classification performance can be comparable with the convolutional neural network (CNN). However, both CNN and Transformer focus excessively on spatial or spectral domain features, resulting in an insufficient combination of spatial-spectral domain information from HSI for modeling. To solve this problem, we propose a new end-to-end graph convolutional network (GCN) and Transformer fusion network with the spatial-spectral feature extraction (GTFN) in this paper, which combines the strengths of GCN and Transformer in both spatial and spectral domain feature extraction, taking full advantage of the contextual information of classified pixels while establishing remote dependencies in the spectral domain compared with previous approaches. In addition, GTFN uses Follow Patch as an input to the GCN and effectively solves the problem of high model complexity while mining the relationship between pixels. It is worth noting that the spectral attention module is introduced in the process of GCN feature extraction, focusing on the contribution of different spectral bands to the classification. More importantly, to overcome the problem that Transformer is too scattered in the frequency domain feature extraction, a neighborhood convolution module is designed to fuse the local spectral domain features. On Indian Pines, Salinas, and Pavia University datasets, the overall accuracies (OAs) of our GTFN are 94.00%, 96.81%, and 95.14%, respectively. The core code of GTFN is released at https://github.com/1useryang/GTFN.

In recent years, the low-rank sparse tracker has been successfully used in object tracking by exploiting low-rank constraints to capture the underlying structure of candidate particles. It uses simple sparse error to account for occlusion and noise measured by the L1-norm, which is assumed to be following the Laplacian distribution. However, this Laplacian assumption may not be accurate to describe complex corruptions. In this letter, we propose an infrared (IR) target tracking method based on a robust low-rank sparse representation which aims to seek for the maximum-likelihood estimation solution of the residuals in the tracking framework. Experimental results on challenging IR image sequences indicate that the proposed method achieves favorable tracking performance and is more robust to various types of noise.

Computer simulation of infrared images is a key step in the simulation of infrared imaging guidance system, and it plays an important role in the research, inspection and evaluation of missile guidance system. Simulation technology of infrared scene based on the OGRE (Object-Oriented Graphics Rendering Engine) is studied in this paper, the real-time infrared scene images of the typical ground target are simulated, and the comparison and analysis of the simulated infrared images and the real infrared images in visual effect and similarity of the gray histogram is made. The results show that the infrared scene simulation framework proposed based on OGRE is feasible and could meet the actual requirement of simulation.