Xinyi Ying

Infrared small target (IRST) detection is challenging in simultaneously achieving precise, robust, and efficient performance due to extremely dim targets and strong interference. Current learning-based methods attempt to leverage “more” information from both the spatial and the short-term temporal domains, but suffer from unreliable performance under complex conditions while incurring computational redundancy. In this paper, we explore the “more essential” information from a more crucial domain for the detection. Through theoretical analysis, we reveal that the global temporal saliency and correlation information in the temporal profile demonstrate significant superiority in distinguishing target signals from other signals. To investigate whether such superiority is preferentially leveraged by well-trained networks, we built the first prediction attribution tool in this field and verified the importance of the temporal profile information. Inspired by the above conclusions, we remodel the IRST detection task as a one-dimensional signal anomaly detection task, and propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection. We conducted extensive experiments to fully validate the effectiveness of our method. The experimental results are exciting, as our DeepPro outperforms existing state-of-the-art IRST detection methods on widely-used benchmarks with extremely high efficiency, and achieves a significant improvement on dim targets and in complex scenarios. We provide a new modeling domain, a new insight, a new method, and a new performance, which can promote the development of IRST detection. Codes are available at https://tinalrj.github.io/DeepPro/ https://tinalrj.github.io/DeepPro/.

Dense infrared small target detection (ISTD) in long-range remote sensing is critical for multi-target surveillance, yet existing benchmarks mostly contain only sparsely distributed targets and rarely reflect dense scenes. To address this limitation, we construct a new dense satellite ISTD dataset, IR-SatDense, by compositing small targets onto real satellite infrared backgrounds and partitioning it into subsets using the Average Minimum Inter-Target Distance (AMID) to explicitly control target density. By visualizing multi-stage backbone features, we observe that in dense scenes the deepest stage naturally forms compact, high-response target clusters in the semantic feature maps, while low- and middle-level features remain heavily cluttered. This motivates us to treat high-level semantic density as a global prior to guide low-level feature enhancement. Therefore, we propose Semantic Density-Guided ResNet (SDG-ResNet), a plug-in backbone that attaches a lightweight semantic density head to the deepest stage and injects the predicted density map into intermediate layers through Semantic Density-Guided Refine (SDGR) blocks with residual spatial gating. Integrated into representative transformer-based detectors, including Deformable DETR, DETA, and DINO, SDG-ResNet consistently improves the probability of detection (PD) at comparable false alarm (FA) levels on IR-SatDense while maintaining competitive performance on the sparse dataset IRSTD-1K.

In recent years, vehicle detection in unmanned aerial vehicle (UAV) videos has attracted significant attention. However, the traditional detection paradigm based on single frame image is faced with the performance bottleneck caused by rare pose, motion blur and occlusion. To address these issues, we propose a novel network comprising three important modules designed to fully exploit the spatiotemporal contextual information across video frames. Firstly, To strengthen the backbone network's capacity for extracting global contextual information from single-frame images, we propose the Visual State Space Context Module (VSSCM). By incorporating the 2D-Selective-Scan module (SS2D), VSSCM captures global dependencies and enriches contextual information without significantly increasing computational complexity. Secondly, the Temporal Information-Guided Spatial Attention Aggregation Module (TGSAM) is introduced to fuse features from critical regions in adjacent frames. Finally, the Self-Attention-based Classification Feature Aggregation Module (SACFAM) is designed to model the relation between object features across frames and perform feature aggregation based on the learned relation matrix, thereby effectively improving the quality of classification features in UAV videos. Extensive experiments are conducted on the challenging VisDrone2019-VID dataset, and the experimental results demonstrate the effectiveness and superiority of the proposed method.

Infrared small targets are typically tiny and locally salient, which belong to high-frequency components (HFCs) in images. Single-frame infrared small target (SIRST) detection is challenging, since there are many HFCs along with targets, such as bright corners, broken clouds, and other clutters. Current learning-based methods rely on the powerful capabilities of deep networks, but neglect explicit modeling and discriminative representation learning of various HFCs, which is important to distinguish targets from other HFCs. To address the aforementioned issues, we propose a dynamic high-frequency convolution (DHiF) to translate the discriminative modeling process into the generation of a dynamic local filter bank. Especially, DHiF is sensitive to HFCs, owing to the dynamic parameters of its generated filters being symmetrically adjusted within a zerocentered range according to Fourier transformation properties. Combining with standard convolution operations, DHiF can adaptively and dynamically process different HFC regions and capture their distinctive grayscale variation characteristics for discriminative representation learning. DHiF functions as a dropin replacement for standard convolution and can be used in arbitrary SIRST detection networks without significant decrease in computational efficiency. To validate the effectiveness of our DHiF, we conducted extensive experiments across different SIRST detection networks on real-scene datasets. Compared to other state-of-the-art convolution operations, DHiF exhibits superior detection performance with promising improvement. Codes are available at https://github.com/TinaLRJ/DHiF.

Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on edge and shape features, but ignore the richer structural differences and detailed information embedded in high-frequency components from different directions, thereby failing to fully exploit the value of high-frequency directional features in target perception. To address this limitation, we propose a multi-scale direction-aware network (MSDA-Net), which is the first attempt to integrate the high-frequency directional features of infrared small targets as domain prior knowledge into neural networks. Specifically, to fully mine the high-frequency directional features, on the one hand, a high-frequency direction injection (HFDI) module without trainable parameters is constructed to inject the high-frequency directional information of the original image into the network. On the other hand, a multi-scale direction-aware (MSDA) module is constructed, which promotes the full extraction of local relations at different scales and the full perception of key features in different directions. In addition, considering the characteristics of infrared small targets, we construct a feature aggregation (FA) structure to address target disappearance in high-level feature maps, and a feature calibration fusion (FCF) module to alleviate feature bias during cross-layer feature fusion. Extensive experimental results show that our MSDA-Net achieves state-of-the-art (SOTA) results on multiple public datasets. The code can be available at https://github.com/YuChuang1205/MSDA-Net.

Visible-thermal small object detection (RGBT SOD) is a significant yet challenging task with a wide range of applications, including video surveillance, traffic monitoring, search and rescue. However, existing studies mainly focus on either visible or thermal modality, while RGBT SOD is rarely explored. Although some RGBT datasets have been developed, the insufficient quantity, limited diversity, unitary application, misaligned images and large target size cannot provide an impartial benchmark to evaluate RGBT SOD algorithms. In this paper, we build the first large-scale benchmark with high diversity for RGBT SOD (namely RGBT-Tiny), including 115 paired sequences, 93 K frames and 1.2 M manual annotations. RGBT-Tiny contains abundant objects (7 categories) and high-diversity scenes (8 types that cover different illumination and density variations). Note that, over 81% of objects are smaller than 16×16, and we provide paired bounding box annotations with tracking ID to offer an extremely challenging benchmark with wide-range applications, such as RGBT image fusion, object detection and tracking. In addition, we propose a scale adaptive fitness (SAFit) measure that exhibits high robustness on both small and large objects. The proposed SAFit can provide reasonable performance evaluation and promote detection performance. Based on the proposed RGBT-Tiny dataset, extensive evaluations have been conducted with IoU and SAFit metrics, including 30 recent state-of-the-art algorithms that cover four different types (i.e., visible generic object detection, visible SOD, thermal SOD and RGBT object detection).

Multiframe infrared small target (MIRST) detection in satellite videos has been a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as follows. First, the extremely small target size, highly complex clutter & noise and various satellite motions result in limited feature representation, high false alarms and difficult motion analyses. In addition, existing methods are primarily designed for static or slightly adjusted perspectives captured by short-distance platforms, which cannot generalize well to complex background motion in satellite videos. Second, the lack of a large-scale publicly available MIRST dataset in satellite videos greatly hinders the algorithm development. To address the aforementioned challenges, in this article, we first build a large-scale dataset for MIRST detection in satellite videos (namely IRSatVideo-LEO), and then develop a recurrent feature refinement (RFR) framework as the baseline method for satellite motion estimation and compensation. Specifically, IRSatVideo-LEO is a semi-simulated dataset with synthesized satellite motion, target appearance, trajectory, and intensity, which can provide a standard toolbox for satellite video generation and a reliable evaluation platform to facilitate algorithm development. For the baseline method, RFR is proposed to be equipped with existing powerful CNN-based methods for long-term temporal dependency exploitation and integrated motion compensation and MIRST detection. Specifically, a pyramid deformable alignment (PDA) module is proposed to achieve effective feature alignment, and a temporal-spatial–frequent modulation (TSFM) module is proposed to achieve efficient feature aggregation and enhancement. Extensive experiments have been conducted to demonstrate the effectiveness and superiority of our scheme. The comparative results show that ResUNet equipped with RFR outperforms the state-of-the-art MIRST detection methods. The dataset and code are available at https://github.com/XinyiYing/RFR.

Event cameras, with high temporal resolution and high dynamic range, have shown great potential under extreme scenarios such as high-speed movement and low illumination. However, previous event representation methods typically aggregate event data into a single dense tensor, often overlooking the dynamic changes of events within a given time unit. This limitation can introduce historical artifacts and semantic inconsistencies, ultimately degrading model performance. Inspired by human visual prior, we propose a motion and appearance decoupling (MAD) event representation to disentangle the mixed spatial-temporal event tensor into two independent branches. This bio-inspired design helps the network extract discriminative temporal (i.e., motion) and spatial (i.e., appearance) information, thus reducing the network's learning burden toward complex high-level interpretation tasks. In our method, the event motion guided attention module (EMGA) is designed to achieve temporal and spatial feature interaction and fusion sequentially. Based on EMGA, three specially designed decoder heads are proposed for several representative event-based tasks (i.e., object detection, semantic segmentation, and human pose estimation). Experimental results demonstrate that our method achieves state-of-the-art performance on the above three tasks, which reveals that our method is an easy-to-implement replacement for currently event-based methods. Our code is available at: https://github.com/ChenYichen9527/MAD-representation.

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.

Neural networks contain considerable redundant computation, which drags down the inference efficiency and hinders the deployment on resource-limited devices. In this paper, we study the sparsity in convolutional neural networks and propose a generic sparse mask mechanism to improve the inference efficiency of networks. Specifically, sparse masks are learned in both data and channel dimensions to dynamically localize and skip redundant computation at a fine-grained level. Based on our sparse mask mechanism, we develop SMPointSeg, SMSR, and SMStereo for point cloud semantic segmentation, single image super-resolution, and stereo matching tasks, respectively. It is demonstrated that our sparse masks are well compatible to different model components and network architectures to accurately localize redundant computation, with computational cost being significantly reduced for practical speedup. Extensive experiments show that our SMPointSeg, SMSR, and SMStereo achieve state-of-the-art performance on benchmark datasets in terms of both accuracy and efficiency.