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- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
- Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation92
This paper introduces a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative laplacian pyramid and directional filter bank to mine the structural texture knowledge and proposes a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation.
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- Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses47
Two multi-phase convolutional neural networks are developed for distinguishing benign from malignant renal masses, and differentiating indolent from aggressive renal tumours in a large cohort of preoperative CT scans, obtaining high accuracy and a strong association with clinical outcomes.
- SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior47
A novel approach is proposed that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data and exploiting depth data from LiDAR point clouds to supply additional spatial information.
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- Context-Aware Graph Convolution Network for Target Re-identification38
A novel Context-Aware Graph Convolution Network (CAGCN), where the probe-gallery relations are encoded into the graph nodes and the graph edge connections are well controlled by the gallery- gallery relations, which achieves state-of-the-art performance on both person and vehicle re-identification datasets in a plug and play fashion with limited overhead.
- Towards faster local search for minimum weight vertex cover on massive graphs36
This paper designs an efficient algorithm named FastWVC to solve MWVC problem in massive graphs, and proposes a construction procedure, which aims to generate a quality initial vertex cover in short time.
- UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction35
A novel Gaussian Splatting-based method, UW-GS, designed specifically for underwater applications, that introduces a color appearance that models distance-dependent color variation, employs a new physics-based density control strategy to enhance clarity for distant objects, and uses a binary motion mask to handle dynamic content.
- ACDC: The Adverse Conditions Dataset With Correspondences for Robust Semantic Driving Scene Perception31
A detailed empirical study demonstrates the challenges that the adverse domains of ACDC pose to state-of-the-art supervised and unsupervised approaches and indicates the value of the dataset in steering future progress in the field.
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- Image-attribute reciprocally guided attention network for pedestrian attribute recognition27
This work proposes an image-attribute reciprocal guidance representation method, which investigates image-guided feature and attribute- guided feature to learn the comprehensive features for different attributes, and develops a fusion attention mechanism.
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- Real-time detection and location of reserved anchor hole in coal mine roadway support steel belt13
The improved YOLOv5s model is improved by adding SPD-Conv and coordinate attention mechanisms to improve the detection capability of the model, and the real-time target detection and location method based on a deep learning model and a depth camera is feasible in an unstructured environment in coal mines.
- Action recognition using linear dynamic systems13
A novel approach based on Linear Dynamic Systems for action recognition that yields the state-of-the-art performance on three benchmark datasets and a supervised codebook pruning method to preserve the discriminative visual words and suppress the noise in each action class.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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