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Works187 from public data
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks8,870
This paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain, and develops a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction.
- Detection and Tracking Meet Drones Challenge1,344
This work provides a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and concludes the challenge as well as propose future directions.
- 1,147
- Progressive Image Deraining Networks: A Better and Simpler Baseline1,052
A better and simpler baseline deraining network by considering network architecture, input and output, and loss functions is provided and is expected to serve as a suitable baseline in future deraining research.
- Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning745
An uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework is proposed, which takes uncertainty as a weight to boost model learning more effectively while reducing bias caused by high-uncertainty objects.
- 562
- Unsupervised feature selection by regularized self-representation323
Experimental analysis on synthetic and real-world data demonstrates that the proposed regularized self-representation (RSR) model can effectively identify the representative features, outperforming many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy, redundancy reduction and classification accuracy.
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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.
- DetFusion: A Detection-driven Infrared and Visible Image Fusion Network246
A detection-driven infrared and visible image fusion network, termed DetFusion, is proposed, which utilizes object-related information learned in the object detection networks to guide multimodal image fusion and outperforms state-of-the-art methods in maintaining pixel intensity distribution and preserving texture details.
- Subspace clustering guided unsupervised feature selection209
Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods and can uncover the underlying multi-subspace structure of data.
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- Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization190
This work proposes a multi-scale patch based CRC method that outperforms many state-of-the-art patch based face recognition algorithms, while the ensemble of multi- scale outputs is achieved by regularized margin distribution optimization.
- Flexible Multi-View Representation Learning for Subspace Clustering174
This paper proposes to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction, and achieves superior clustering performance over other state-ofthe-art methods.
- Vision Meets Drones: Past, Present and Future173
The VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South, is described, being the largest such dataset ever published, and enables extensive evaluation and investigation of visual analysis algorithms on the drone platform.
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- Multi-label feature selection with missing labels170
This paper presents a novel multi-label feature selection model under the circumstance of missing labels, where the most discriminative features are selected and missing labels are recovered simultaneously.
- Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model160
An adaptive robust multi-kernel regression model is proposed that provides more accurate deterministic forecasts and more useful probabilistic forecasts, and has great potential for practical application in power systems.
- Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion152
Extensive experiments show that the proposed MoE-Fusion outperforms state-of-the-art methods in preserving multi-modal image texture and contrast through the local-to-global dynamic learning paradigm, and also achieves superior performance on detection tasks.
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- Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark139
The Space-Time Neighbor-Aware Network (STNNet) is designed as a strong baseline to solve object detection, tracking and counting jointly in dense crowds and performs favorably against the state-of-the-arts.
- 1 Image Set based Collaborative Representation for Face Recognition138
The proposed model naturally and effectively extends the image-based collaborative representation to an image set based one, and the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency is shown.
- Spatial Attention Pyramid Network for Unsupervised Domain Adaptation137
A new spatial attention pyramid network that combines the dense global structure representation and local texture patterns at each spatial location effectively using the spatial attention mechanism to focus on the discriminative regions with context information for domain adaption.
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 2026-10-13. 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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