Jianwei Zhang

Text-based person search aims to retrieve person images matching a given textual description. The challenge lies in mapping images and textual descriptions into a unified semantic space. This paper introduces ADT-Net, a novel framework designed to address the issue of excessive intra-class variance and insufficient inter-class variance caused by lighting variations. ADT-Net comprises two key modules: Invariant Representation Learning (IRL), which employs style transfer strategies and multi-scale alignment techniques to learn visually invariant features, and Dynamic Matching Alignment (DMA), which introduces nonlinear transformations and learnable dynamic temperature parameters to optimize the prediction distribution. Experimental results on multiple benchmark datasets demonstrate that ADT-Net outperforms current mainstream baseline methods, achieving superior retrieval accuracy and generalization ability. Here, we show that our proposed method significantly enhances the robustness of cross-modal person retrieval, particularly under varying lighting conditions and shooting angles. Code is available at https://github.com/2Elian/ADT-Net.

With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their high computational demands, slow convergence rates, and inadequacy in managing dense, multi-scale objects. These challenges are especially acute in remote sensing applications, where accurate analysis of large-scale aerial and satellite imagery relies heavily on effective dense object detection. In this paper, we propose the DHQ-DETR framework, which addresses these issues by modeling bounding box offsets as distributions. DHQ-DETR incorporates the Distribution Focus Loss (DFL) to enhance residual learning, and introduces a High-Quality Query Selection (HQQS) module to effectively balance classification and regression tasks. Additionally, we propose an auxiliary detection head and a sample assignment strategy that complements the Hungarian algorithm to accelerate convergence. Our experimental results demonstrate the superior performance of DHQ-DETR, achieving an average precision (AP) of 53.7% on the COCO val2017 dataset, 54.3% on the DOTAv1.0, and 32.4% on Visdrone, underscoring its effectiveness for real-world dense object detection tasks.

Clouds are an important factor in predicting future weather changes. Cloud image classification is one of the basic issues in the field of ground-based cloud meteorological observation. Deep CNN mainly focuses on the local receptive field, and the processing of global information may be relatively weak. In ground-based cloud image classification, if there is a complex background, it will help to better model the long-range dependence of the image if the relationship between different locations in the image can be globally captured. A ground-based cloud image classification method is proposed based on the fusion of local features and global features (LG_CloudNet). The ground-based cloud image classification method integrates the global feature extraction module (GF_M) and the local feature extraction module (LF_M), using the attention mechanism to weight and merge features, respectively. The LG_CloudNet model enables richer and comprehensive feature representation at lower computational complexity. In order to ensure the learning and generalization capabilities of the model during training, AdamW (Adam weight decay) is combined with learning rate warm-up and stochastic gradient descent with warm restarts methods to adjust the learning rate. The experimental results demonstrate that the proposed method achieves favorable ground-based cloud image classification outcomes and exhibits robust performance in classifying cloud images. In the datasets of GCD, CCSN, and ZNCL, the classification accuracy is 94.94%, 95.77%, and 98.87%, respectively.

Small object detection poses significant challenges in the realm of general object detection, primarily due to complex backgrounds and other instances interfering with the expression of features. This research introduces an uncomplicated and efficient algorithm that addresses the limitations of small object detection. Firstly, we propose an efficient cross-scale feature fusion attention module called ECFA, which effectively utilizes attention mechanisms to emphasize relevant features across adjacent scales and suppress irrelevant noise, tackling issues of feature redundancy and insufficient representation of small objects. Secondly, we design a highly efficient convolutional module named SEConv, which reduces computational redundancy while providing a multi-scale receptive field to improve feature learning. Additionally, we develop a novel dynamic focus sample weighting function called DFSLoss, which allows the model to focus on learning from both normal and challenging samples, effectively addressing the problem of imbalanced difficulty levels among samples. Moreover, we introduce Wise-IoU to address the impact of poor-quality examples on model convergence. We extensively conduct experiments on four publicly available datasets to showcase the exceptional performance of our method in comparison to state-of-the-art object detectors.

The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and speed at the same time for UAV tracking. In this paper, an innovative RGBT Siamese tracker named SiamCAF is proposed, which utilizes multi-modal features with a beyond-real-time running speed. Specifically, we used a dual-modal Siamese subnetwork to extract features. In addition, to extract similar features and reduce the modality differences for fusing features efficiently, we designed the Complementary Coupling Feature fusion module (CCF). Simultaneously, the Residual Channel Attention Enhanced module (RCAE) was designed to enhance the extracted features and representational power. Furthermore, the Maximum Fusion Prediction module (MFP) was constructed to boost performance in the response map fusion stage. Finally, comprehensive experiments on three real RGBT tracking datasets and one visible–thermal UAV tracking dataset showed that SiamCAF outperforms other tracking methods, with a remarkable tracking speed of over 105 frames per second.

SSD is a classical single-stage object detection algorithm, which predicts by generating different scales of feature maps on different convolutional layers. However, due to the problems of its insufficient non-linearity and the lack of semantic information in the shallow feature maps, as well as the fact that small objects contain few pixels, the detection accuracy of small objects is significantly worse than that of large- and medium-scale objects. Considering the above problems, we propose a novel object detector, self-attention combined feature fusion-based SSD for small object detection (SAFF-SSD), to boost the precision of small object detection. In this work, a novel self-attention module called the Local Lighted Transformer block (2L-Transformer) is proposed and is coupled with EfficientNetV2-S as our backbone for improved feature extraction. CSP-PAN topology is adopted as the detection neck to equip feature maps with both low-level object detail features and high-level semantic features, improving the accuracy of object detection and having a clear, noticeable and definitive effect on the detection of small targets. Simultaneously, we substitute the normalized Wasserstein distance (NWD) for the commonly used Intersection over Union (IoU), which alleviates the problem wherein the extensions of IoU-based metrics are very sensitive to the positional deviation of the small objects. The experiments illustrate the promising performance of our detector on many datasets, such as Pascal VOC 2007, TGRS-HRRSD and AI-TOD.

Hyperspectral compressive imaging has taken advantage of compressive sensing theory to capture spectral information of the dynamic world in recent decades of years, where an optical encoder is employed to compress high dimensional signals into a single 2-D measurement. The core issue is how to reconstruct the underlying hyperspectral image (HSI), although deep neural network methods have achieved much success in compressed sensing image reconstruction in recent years, they still have some unsolved issues, such as tradeoffs between performance and efficiency, and accurate exploitation of cubic structure information. In this article, we propose a deep Tucker decomposition and spatial–spectral learning network (DS-net) to learn the tensor low-lank structure features and spatial–spectral correlation of HSI for reconstruction quality promotion. Inspired by tensor decomposition, we first construct a deep Tucker decomposition module to learn the principal components from different modes of the image features. Then, we cascade a series of decomposition modules to learn multihierarchical features. Furthermore, to jointly capture the spatial–spectral correlation of HSI, we propose a spatial–spectral correlation learning module in a U-net structure for more robust reconstruction performance. Finally, experimental results on both synthetic and real datasets demonstrate the superiority of the proposed method compared to several state-of-the-art methods in quantitative assessment and visual effects.