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- UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles282
A fisheye-based action recognition method that mitigates the distortions in fisHEye videos via learning unbounded transformations guided by flat RGB videos is proposed, and experiments show the efficacy of this method on the UAV-Human dataset.
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- Mutuality-oriented reconstruction and prediction hybrid network for video anomaly detection20
In the MORPH-Net, a new Mutuality-oriented Training (MO-Training) mechanism is introduced to better combine the advantages of prediction-based models and reconstruction- based models.
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- Attention Bilinear Pooling for Fine-Grained Classification15
A novel convolutional neural network framework, i.e., attention bilinear pooling, for fine-grained classification with attention, which can learn the distinctive feature information from the channel or spatial attention to better represent image features.
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- EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation6
EraW-Net is proposed, a novel end-to-end framework for scene-associated driver attention estimation by aggregating information from dual views that enhances the most discriminative dynamic cues, refines feature representations, and facilitates semantically aligned cross-domain integration through a W-shaped architecture, termed W-Net.
- MultiFuser: Multimodal Fusion Transformer for Enhanced Driver Action Recognition6
A novel multimodal fusion transformer, named Multi-Fuser, which identifies cross-modal interrelations and interactions among multimodal car cabin videos and adaptively integrates different modalities for improved representations is proposed.
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- FNSAM: Image super-resolution using a feedback network with self-attention mechanism3
Experimental results show that the proposed FNSAM obtains more reasonable SR reconstruction of brain MRI images both in peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) than some state-of-the-arts methods.
- Calculation method of projection point of circle center in camera calibration3
The proposed method can get the projection point of circle center accurately and stably, and does not have intrinsic projection error, and the accuracy of the proposed method is 1.71nm higher than that of the direct ellipse fitting method.
- TDS-CLIP: Temporal Difference Side Network for Efficient Video Action Recognition2
A novel memory-efficient Temporal Difference Side Network (TDS-CLIP) is proposed to balance knowledge transferring and temporal modeling, avoiding backpropagation in frozen parameter models and introduces a Temporal Difference Adapter (TD-Adapter), which can effectively capture local temporal differences in motion features to strengthen the model's global temporal modeling capabilities.
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- Hybrid Feature Learning for Crystal System and Space Group Classification from Standard XRD Patterns1
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- Super-Resolution Reconstruction of Brain MRI Images Based on Differential Curvature Grouping Mixture Model1
A super-resolution MRI reconstruction method is proposed based on differential curvature grouping mixture model to reconstruct high-resolution image patches based on differential curvature grouping mixture model.
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- Online Social Network User Behavior Analysis — With RenRen Case1
Analysis of typical user behaviors in RenRen is investigated and a clustering algorithm that assigns users to groups through a distance measure that is computed based on the values of user feature vector is used.
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- ACF-net: appearance-guided content filter network for video captioning–
This work proposes a new multimodal fusion method named ACF-Net: Appearance-guided Content Filter Network, utilizing appearance information as a Content Filter to guide the network to aware discrimination information from both motion information and object information.
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- Spatial Statistics Analysis with Artificial Neural Network–
This work expands the classical spatial autoregressive model to include time lagged observations, related exogenous variables, possibly non-Gaussian, high volatility errors, and a nonlinear neural network component to allow for more model flexibility.
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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 2026-10-11. 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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