As an important field in information retrieval, fine-grained cross-modal retrieval has received great attentions from researchers. Existing fine-grained cross-modal retrieval methods made several improvements in capturing the fine-grained interplay between vision and language, failing to consider the fine-grained correspondences between the features in the image latent space and the text latent space respectively, which may lead to inaccurate inference of intra-modal relations or false alignment of cross-modal information. Considering that object detection can get the fine-grained correspondences of image region features and the corresponding semantic features, this paper proposed a novel latent space semantic supervision model based on knowledge distillation (L3S-KD), which trains classifiers supervised by the fine-grained correspondences obtained from an object detection model by using knowledge distillation for image latent space fine-grained alignment, and by the labels of objects and attributes for text latent space fine-grained alignment. Compared with existing fine-grained correspondence matching methods, L3S-KD can learn more accurate semantic similarities for local fragments in image-text pairs. Extensive experiments on MS-COCO and Flickr30K datasets demonstrate that the L3S-KD model consistently outperforms state-of-the-art methods for image-text matching.

In many military and civilian applications, synthetic aperture radar (SAR) image target detection plays a vital role. However, current methods for SAR target detection generally fail to balance speed and accuracy, thus making it impossible to deploy them to real-world engineering applications. In addition, strong scattering, multi scales, high density, complex background interference and speckle noise make it remarkably challenging to extract effective target information and disentangle background noise from the target information, ultimately resulting in high missing and false alarm rates. To address these issues, a unified dynamic SAR target detection architecture (DAFDet) with asymptotic fusion enhancement and feature encoding decoupling is proposed in this paper. Firstly, a dynamic architecture is constructed by cascading two identical detectors and integrating a designed decision maker. This decision maker can automatically decide the inference route by calculating the difficulty score of a SAR image, which ensures efficient inference speed while achieving high accuracy. Secondly, an asymptotic fusion enhancement feature pyramid network (AFEFPN) is developed, which can avoid the loss and degradation of target information in multi-stage transmissions through direct interactions of nonadjacent levels, thereby enhancing the extraction of valid target information. Suppression of background noise is achieved by modeling the importance of different feature channels of the fused features. Finally, a task-oriented decoupled head (TODH) is proposed to boost the localization and classification abilities of the model in complex scenarios. It decouples feature encoding at the source, thus providing task-oriented feature context. Numerous experiments on four widely adopted datasets reveal that DAFDet obtains efficient inference speed and optimal detection accuracy, achieving new state-of-the-art target detection performance. Source code will be provided at https://github.com/yangyahu1994/DAFDet.

Target detection on synthetic aperture radar (SAR) imagery is critical for several military and civilian applications. However, the coherent imaging mechanism of SAR will inevitably generate speckle noise, reducing the signal-to-noise ratio of SAR imagery, resulting in the loss of certain important information related to texture and thus degrading the accuracy of SAR image target detection. In addition, learning irrelevant features, such as natural clutter, increases the false alarm rate. To address these issues, this paper proposes a high-accurate and low-latency SAR target detection network (ADERLNet) to enhance the ability of the target detection model to represent valid features from strongly noisy images and irrelevant features. Specifically, the deep target detection architecture incorporates soft thresholding functions as nonlinear transformation layers, which can eliminate noisy and irrelevant features by forcing these interference features to be zeros. Second, given that manually setting the appropriate thresholds in the soft thresholding functions is challenging, the thresholds in ADERLNet are automatically determined to avoid substantial signal processing expertise by integrating specially designed sub-networks as trainable modules. Finally, the classification and regression losses are redesigned to offer the model stronger generalization ability by balancing the positive and negative samples and handling the impact of possible low-quality samples in the training set. Additionally, the structural re-parameterization method is employed to balance accuracy and speed. Experimental results on three widely used datasets for SAR target detection, including SSDD, MSAR-1.0, and SAR-Ship-Dataset, reveal that ADERLNet maximally improves average precision (AP) by 2.7%, 3.3%, and 1.8%, respectively, and far outperforms other methods on detection accuracy while maintaining excellent real-time performance, fully highlighting the superiority of ADERLNet. The trained models and source codes will be available at https://github.com/yangyahu-1994/ADERLNet.

This study investigated the effect of the angle between the blade and the inclined disc on particle trajectory correction during ejection from an organic fertilizer side-throwing device. Using the inclined disc device as the test subject, a blade-based coordinate system was established to model the complex relative particle motion under combined disc and blade inclination. Particle dynamics and blade forces were analyzed quadrantally, enabling the development of a mechanical model and the derivation of displacement equations. Numerical simulation, virtual simulation, and experimental testing yielded the following results: Under the current device parameters, the relative velocity between particles and the blade reaches its maximum when the angle between the blade and the inclined disc is 80°. Within the angle range from 65° to 85°, as the angle increases, the scattering angle of single-sided discs monotonically decreases, while that of dual-sided discs monotonously increases. At an angle of 65°, the trajectories of the dual-sided disc flows tend to converge. At 80°, the flow is at the critical point between convergence and divergence. The effective throwing distance first increases and then decreases, reaching a maximum at an angle of 80°. This study clarifies the relationship between the angle correction of blade–disc inclination and particle velocity and trajectory on the blade, providing a reliable mathematical model and simulation method for similar studies in the field of inclined disc centrifugal material ejection.