Undoubtedly, the object is the primary factor in 3D single-object tracking (SOT) tasks. However, prior Siamese-based trackers overlook the adverse effects resulting from randomly dropped object points during backbone sampling, hindering the prediction of accurate bounding boxes (BBoxes). Therefore, developing an approach that maximizes the preservation of object points and their object-aware features is of the utmost significance. To address this, we propose an object-preserving Siamese network (OPSNet) that can effectively maintain object integrity and boost tracking performance. First, anobject highlighting moduleamplifies the object-aware features and extracts discriminative features from the template and search area. Next,object-preserving samplingselects object candidates, obtains object-preserving search area seeds, and discards background points that have less impact on tracking. Finally, anobject localization networkaccurately locates 3D BBoxes based on the object-preserving search area seeds. Extensive experiments demonstrate that the performance of OPSNet exceeds the state-of-the-art performance, achieving success gains of$\sim$9.4% and$\sim$2.5% on the KITTI and Waymo Open datasets, respectively.

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that cannot be collected in advance, which may restrict the performance of fault diagnosis methods. In this article, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specificBarlow matrixis designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods.

Since both visible and infrared images have their own advantages and disadvantages, RGBT tracking plays an important role in intelligent transportation systems. The key points of RGBT tracking lie in feature extraction and fusion of visible and infrared images. Current RGBT tracking methods mostly pay attention to both individual features (features extracted from images of a single camera) and common features (features extracted and fused from an RGB camera and a thermal camera). Still, they pay less attention to different and dynamic contributions of the individual and common features for different sequences of registered image pairs. This paper proposes a novel RGBT tracking method, called Dynamic Fusion Network (DFNet), which adopts a two-stream structure, in which two non-shared convolution kernels are employed in each layer to extract individual features. Besides, DFNet has shared convolution kernels for each layer to extract common features. Since non-shared and shared convolution kernels are adaptively weighted and summed according to different image pairs, DFNet can deal with different contributions for different sequences. DFNet has a fast speed, which is 28.658 FPS. The experimental results show that when DFNet only increases the Mult-Adds by 0.02% compared with the non-shared-convolution-kernel-based fusion method, Precision Rate (PR) and Success Rate (SR) reach 88.1% and 71.9%, respectively. The model and dataset are available athttps://github.com/PengJingchao/DFNet.