Visible–infrared object detection aims to improve the detector performance by fusing the complementarity of visible and infrared images. However, most existing methods only use local intramodality information to enhance the feature representation while ignoring the efficient latent interaction of long-range dependence between different modalities, which leads to unsatisfactory detection performance under complex scenes. To solve these problems, we propose a feature-enhanced long-range attention fusion network (LRAF-Net), which improves detection performance by fusing the long-range dependence of the enhanced visible and infrared features. First, a two-stream CSPDarknet53 network is used to extract the deep features from visible and infrared images, in which a novel data augmentation (DA) method is designed to reduce the bias toward a single modality through asymmetric complementary masks. Then, we propose a cross-feature enhancement (CFE) module to improve the intramodality feature representation by exploiting the discrepancy between visible and infrared images. Next, we propose a long-range dependence fusion (LDF) module to fuse the enhanced features by associating the positional encoding of multimodality features. Finally, the fused features are fed into a detection head to obtain the final detection results. Experiments on several public datasets, i.e., VEDAI, FLIR, and LLVIP, show that the proposed method obtains state-of-the-art performance compared with other methods.

Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods leverage both visual and depth representations to explore valuable query points on objects, and the quality of the learned query points has a great impact on detection accuracy. Unfortunately, existing unsupervised attention mechanisms in transformers are prone to generate low-quality query features due to inaccurate receptive fields, especially on hard objects. To tackle this problem, this paper proposes a novel “Supervised Scale-aware Deformable Attention” (SSDA) for monocular 3D object detection. Specifically, SSDA presets several masks with different scales and utilizes depth and visual features to adaptively learn a scale-aware filter for object query augmentation. Imposing the scale awareness, SSDA could well predict the accurate receptive field of an object query to support robust query feature generation. Aside from this, SSDA is assigned with a Weighted Scale Matching (WSM) loss to supervise scale prediction, which presents more confident results as compared to the unsupervised attention mechanisms. Extensive experiments on the KITTI and Waymo Open datasets demonstrate that SSDA significantly improves the detection accuracy, especially on moderate and hard objects, yielding state-of-the-art performance as compared to the existing approaches. Our code will be made publicly available at https://github.com/mikasa3lili/SSD-MonoDETR .

Visible-infrared object detection has attracted increasing attention recently due to its superior performance and cost-efficiency. Most existing methods focus on the detection of strictly-aligned data, significantly limiting its practical applications. Although several researchers have attempted to explore weakly-aligned visible-infrared object detection, they are limited to small translational deviations and suffer from a low detection speed. This paper first explores non-aligned visibleinfrared object detection with complex deviations in translation, scaling, and rotation, and proposes a fast one-stage detector YOLO-Adaptor, which introduces a lightweight multi-modal adaptor to simultaneously predict alignment parameters and confidence weights between modalities. The adaptor adopts a feature-level alignment during the feature extraction process, ensuring high alignment efficiency. Moreover, we introduce a feature contrastive learning loss to guide the alignment learning of the adaptor, aiming to reduce the representation gap between the two modalities in hyperbolic space to implement feature spatial and distributional consistency. Extensive experiments are conducted on three datasets, including one weakly-aligned and two non-aligned datasets, and the experimental results demonstrate that YOLO-Adaptor could achieve significant performance improvements in terms of speed and accuracy

Weakly aligned visible-infrared object detection poses significant challenges due to the imprecise alignment between visible and infrared images. Most existing methods explore the alignment strategies between visible and infrared images, yielding unbearable computation costs. This paper first proposes an end-to-end alignment-free architecture Cross-modal Fusion Deformable DEtection TRansformer (``CF-Deformable DETR'') for weakly aligned visible-infrared object detection. Abandoning the traditional image alignment, CF-Deformable DETR introduces a simple yet effective cross-modal deformable attention mechanism to directly implement automatic cross-modal point mapping, generating well-aligned bimodal features with high efficiency. Moreover, we design a Point-level Feature Consistency Loss to guide the cross-modal point mapping, ensuring the consistency of paired features to support the following fusion. Extensive experiments are conducted on three benchmark datasets. The experimental results demonstrate that CF-Deformable DETR achieves close accuracy on weakly aligned and strictly aligned data as well as maintains stable performance to a certain extent against various offset degrees of weakly aligned data. Code is available at https://github.com/116508/CF-Deformable-DETR.

This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm.