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Works409 from public data
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- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results259
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
- Multimodal medical image fusion based on IHS and PCA258
Visual and quantitative analysis show that the proposed algorithm significantly improves the fusion quality; compared to fusion methods including PCA, Brovey, discrete wavelet transform (DWT).
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- Key-Word-Aware Network for Referring Expression Image Segmentation200
A key-word-aware network, which contains a query attention model and a key-words-aware visual context model, which outperforms state-of-the-art methods on two referring expression image segmentation databases.
- Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm188
A greedy heuristic dynamic programming iteration algorithm is developed to solve the zero-sum game problems, which can be used to solves the Hamilton-Jacobi-Isaacs equation associated with H"~ optimal regulation control problems.
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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.
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- CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints111
This paper proposes a novel hierarchical search-based solver called Car-like Conflict-Based Search to address the Multi-Agent Path Finding for Car-Like robots problem, and introduces a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotem temporal constraints.
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- Saliency model-based face segmentation and tracking in head-and-shoulder video sequences98
A novel face segmentation algorithm based on facial saliency map (FSM) for head-and-shoulder type video application that is capable of segmenting the face area quite effectively.
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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 2026-10-15. 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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