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- TransBTS: Multimodal Brain Tumor Segmentation Using Transformer1,261
This paper exploits Transformer in 3D CNN for MRI Brain Tumor Segmentation and proposes a novel network named TransBTS based on the encoder-decoder structure that achieves comparable or higher results than previous state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans.
- Infrared and visible image fusion via gradient transfer and total variation minimization1,127
A novel fusion algorithm, named Gradient Transfer Fusion (GTF), based on gradient transfer and total variation (TV) minimization is proposed, which can keep both the thermal radiation and the appearance information in the source images.
- Deep Learning-based Human Pose Estimation: A Survey1,051
This survey article is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures.
- Multi-Scale Progressive Fusion Network for Single Image Deraining859
This work explores the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi- scale progressive fusion network (MSPFN) for single image rain streak removal.
- Enhanced skeleton visualization for view invariant human action recognition850
Enhanced skeleton visualization method encodes spatio-temporal skeletons as visual and motion enhanced color images in a compact yet distinctive manner and consistently achieves the highest accuracies on four datasets, including the largest and most challenging NTU RGB+D dataset for skeleton-based action recognition.
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- 3D Human Pose Estimation with Spatial and Temporal Transformers731
PoseFormer is presented, a purely transformer-based approach for 3D human pose estimation in videos without convolutional architectures involved, designed to comprehensively model the human joint relations within each frame as well as the temporal correlations across frames.
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- Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion471
This work proposes a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training and has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.
- Infrared and visible image fusion via detail preserving adversarial learning456
This paper proposes an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning that is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods.
- Gabor Convolutional Networks420
A new deep model is proposed, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes.
- Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos375
Tube Convolutional Neural Network (T-CNN) is proposed, a unified deep network that is able to recognize and localize action based on 3D convolution features that demonstrates the superior performance of T-CNN for classifying and localizing actions in both trimmed and untrimmed videos compared to state-of-the-arts.
- A survey of depth and inertial sensor fusion for human action recognition359
The thrust of this survey is on the utilization of depth cameras and inertial sensors as these two types of sensors are cost-effective, commercially available, and more significantly they both provide 3D human action data.
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- TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization331
A pure transformer-based approach to address cross-view image geo-localization limitations from a different perspective, which achieves state-of-the-art results on both urban and rural datasets, with significantly less computation cost than CNN-based methods.
- Real-time human action recognition based on depth motion maps314
A l2-regularized collaborative representation classifier with a distance-weighted Tikhonov matrix is employed for action recognition, shown to be computationally efficient allowing it to run in real-time.
- Density Map Guided Object Detection in Aerial Images311
This paper proposes a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
- AIM: Adapting Image Models for Efficient Video Action Recognition273
This work proposes a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding, and shows that the proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks.
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- PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation260
PoseFormerV2 is proposed, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection.
- 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.
- Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning250
This work considers rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction, and finds that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects.
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- Hyperspectral Image Classification in the Presence of Noisy Labels203
Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of the proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive.
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 2026-10-14. 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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