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- Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition1,225
A simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D are presented and a powerful feature extractor named MS-G3D is developed based on which the model outperforms previous state-of-the-art methods on three large-scale datasets.
- Study on subset size selection in digital image correlation for speckle patterns646
A theoretical model of the displacement measurement accuracy of DIC can be accurately predicted based on the variance of image noise and Sum of Square of Subset Intensity Gradients (SSSIG), which leads to a simple criterion for choosing an optimal subset size for the DIC analysis.
- Video summarization via minimum sparse reconstruction185
This paper formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem, where the original video sequence can be best reconstructed with as few selected keyframes as possible.
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- Learning visual relationship and context-aware attention for image captioning154
A novel method to implicitly model the relationship among regions of interest in an image with a graph neural network, as well as a novel context-aware attention mechanism to guide attention selection by fully memorizing previously attended visual content are proposed.
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- Graph weeds net: A graph-based deep learning method for weed recognition126
A novel graph-based deep learning architecture, namely Graph Weeds Net (GWN), which aims to recognize multiple types of weeds from conventional RGB images collected from complex rangelands, and provides suggestions for key regions, creating opportunities for further within-image actions for robotic in-field systems.
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- Leaf Image Retrieval with Shape Features103
An efficient two-step approach of using a shape characterization function called centroid-contour distance curve and the object eccentricity (or elongation) for leaf image retrieval and Experimental results show that this approach can achieve good performance with a reasonable computational complexity.
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- Deep learning techniques for in-crop weed recognition in large-scale grain production systems: a review93
This paper reviews recent developments of deep learning techniques in the field of image-based weed detection, and the challenges of developing practically deployable weed detection methods are summarized.
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- The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio90
This work demonstrates that ADD model training with the Codecfake dataset can effectively detects ALM-based audio, and proposes the CSAM strategy to learn a domain balanced and generalized minima to achieve universal detection of deepfake audio.
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- Video summarization via block sparse dictionary selection78
Experimental results on two benchmark datasets demonstrate that the proposed SBOMP based VS method clearly outperforms several state-of-the-art sparse representation based methods in terms of F-score, redundancy among keyframes and robustness to outlier frames.
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- Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems65
This review looks at the progress made over this half-century of research, summarizing advances in computer vision techniques and the most recent deep convolutional neural network approaches to weed recognition; and shows that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition.
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- Sign Language Translation with Hierarchical Spatio-Temporal Graph Neural Network64
A novel deep learning architecture, namely hierarchical spatio-temporal graph neural network (HST-GNN), is proposed and graph convolutions and graph self-attentions with neighborhood context are proposed to characterize both the local and the global graph properties.
- Stacked Memory Network for Video Summarization63
A stacked memory network called SMN is proposed to explicitly model the long dependency among video frames so that redundancy could be minimized in the video summaries produced and is particularly good at capturing long temporal dependency among frames with few additional training parameters.
- LiDARCap: Long-range Markerless 3D Human Motion Capture with LiDAR Point Clouds62
Qualitative and qualitative experiments show that the LiDARHuman26M method outperforms the techniques based only on RGB images, and the experiments on the KITTI Dataset and the Waymo Open dataset show that it can be generalized to different LiDar sensor settings.
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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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