Object detection in UAV imagery presents a significant challenge due to low image resolution, complex visual backgrounds, and substantial inter-object scale variation. Detection Transformer (DETR)-based models often underperform in such scenarios. This paper proposes Pre-Fusion Guided DETR (PF-DETR), a refined and enhanced extension of the D-FINE model, specifically designed for robust small-object detection in UAV imagery. PF-DETR introduces a novel Pre-Encoded Feature Fusion (PEFF) module, which employs a unidirectional fusion strategy to integrate low-level spatial features into high-level semantic features, thereby improving the model’s ability to capture fine-grained details of small objects. Additionally, a Feature Enhancement Attention (FEA) mechanism is incorporated to strengthen feature representation and reduce background interference. To address information loss during multi-scale processing, a Haar Wavelet Downsampling (HWD) module is proposed, combining Haar wavelet transforms with convolutional downsampling to better preserve critical features. Furthermore, the original GIoU loss is replaced with an Enhanced IoU (EIoU) loss function to improve localization accuracy and training efficiency. Experimental results on the challenging VisDrone2019 benchmark demonstrate that PF-DETR outperforms state-of-the-art detectors, including DEIM, D-FINE, YOLOv10, and YOLOv11, in both detection accuracy and robustness. Specifically, PF-DETR achieves an AP50 of 40.6
The Knowledge Graph (KG) augmented recommendation mitigates the cold start issue by exploiting complex semantic clues in users' behaviors. However, existing methods focus on bringing these auxiliary knowledge into the user-item interaction space, ignoring the gap between different sources. In this study, we propose a knowledge-aware intent subgraph learning (KISL) which mines users' intents with KG to promote fine-grained interest learning for personalized recommendation. We model each intent representation as an attentive combination of KG relations on the knowledge graph. Guided by the intents, KISL devises a dimensional disentanglement to divide the interaction graph into several augmented intent-aware subgraphs. Fine-grained personalized embedding is learned during subgraph message propagation to predict users' interactions. Extensive experiments on two public datasets demonstrate the effectiveness of KISL by knowledge-aware intent modeling over baselines.
Point-cloud denoising should suppress noise without harming geometry, classical approaches often oversmooth complex object surfaces, erasing fine details. Although recent learning-based techniques alleviate these shortcomings, they still face two critical limitations: (i) single-scale feature extractors without explicit positional encodings struggle to capture long-range dependencies, and (ii) stage-wise pipelines that update normals and coordinates separately accumulate errors. We propose an end-to-end framework that fuses multi-scale convolutions with a position-encoded Transformer and a geometry-guided feedback loop. Convolutions capture local detail, the Transformer models global relations, and residual fusion unifies their features. A key-point selector, driven by normal orthogonality and cross-scale agreement, retains only high-confidence points, while a bidirectional module jointly refines coordinates and normals under a geometric-consistency loss. On synthetic CAD and non-CAD datasets corrupted with 0.6– 2.0 % Gaussian noise, the method achieves lower Chamfer and point-to-surface distances than five state-of-the-art baselines. Ablations confirm each component’s value, and qualitative results preserve sharp edges and high curvature, providing an efficient, practical solution for point-cloud denoising.
Medical image segmentation task plays an important role in areas such as clinical care and medical atlas construction, but existing 2D segmentation models often face challenges in 3D organ or tissue segmentation tasks, and 3D models account for a large computing resource proportion. Therefore, we consider 3D tissue organs as video sequences and use the given first layer of tissue organ boundaries as a sequence of query points to predict matching points on subsequent slices. Based on this idea, we propose a point tracking architecture optimised based on the joint point tracking model CoTracker, which compensates for the shortcomings of CNN-based architectures in global feature information extraction and improves the robustness of the overall image features by incorporating the robust, strongly generalisable DINOv2 encoder. The model uses semantic features extracted by the self-supervised learning foundational model DINOv2-ViT for feature fusion with ResNet, and optimises them with a fine-tuning strategy based on the CoTracker weights. Specifically, we introduce the channel attention mechanism to make full use of Vision Transformer's feature recognition capability, and achieve feature optimisation by filtering high-weighted channels, thus improving the accuracy of several evaluation metrics in the point tracking domain. Extensive evaluation results show that our approach not only greatly saves training resources, but also efficiently improves tracking accuracy and has good generalisation in the field of medical image segmentation.
Single-stage 3D object detection based on pillar structures has gained attention for its high inference efficiency in autonomous driving. However, the quantization of point clouds into pillars often leads to the loss of fine-grained structural details, limiting performance in detecting small objects under sparse scenarios. To overcome this challenge, we propose an efficient LiDAR-based detection algorithm that incorporates multi-source feature encoding and structure-aware enhancement. Specifically, we design a Structure-Aware Feature Encoding (SAFE) module that integrates intra-pillar attention, inter-pillar structural modeling, and height histogram encoding to improve the geometric representation of pillar features. The backbone further incorporates a large kernel attention mechanism to capture long-range dependencies, along with an atrous spatial pyramid pooling module and a weighted dual-scale feature fusion module to strengthen semantic expressiveness and detection accuracy. The proposed method is evaluated on the KITTI dataset, with extensive visualizations and ablation studies. Results show an improvement of 3.9
