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- An interpretable mortality prediction model for COVID-19 patients1,312
Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.
- Learning to Upsample by Learning to Sample767
Dysample, an ultra-lightweight and effective dynamic upsampler that requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency, outperforms other upsamplers across five dense prediction tasks.
- 300
- TasselNet: counting maize tassels in the wild via local counts regression network270
This is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment and TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy.
- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results263
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.
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- A fast and robust local descriptor for 3D point cloud registration228
This paper proposes a novel local feature descriptor, called a local feature statistics histogram (LFSH), for efficient 3D point cloud registration, and an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences.
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- P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds191
This work first localize potential target centers in 3D search area embedded with target information, then point-driven 3D target proposal and verification are executed jointly, so the time-consuming 3D exhaustive search can be avoided.
- From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer191
S-DCNet achieves the state-of-the-art performance on three crowd counting datasets, a vehicle counting dataset (TRANCOS) and a plant counting datasets (MTC), and can generalize to open-set counts via S-DC.
- A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image190
The proposed 3D articulated pose estimation paradigm is different from the state-of-the-art encoder-decoder based FCN, 3D CNN and point-set based manners and 2D CNN is used as backbone network to drive A2J, without using time-consuming 3D convolutional or deconvolutional layers.
- TOLDI: An effective and robust approach for 3D local shape description171
Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of the TOLDI method for local shape description.
- TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks159
TasselNetv2 for counting wheat spikes is described, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity, and improving efficiency without sacrificing accuracy.
- Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting146
A similarity-aware CAC framework that jointly learns representation and similarity metric and extends BMNet to BMNet+ that models similarity from three aspects that significantly outperform state-of-the-art CAC approaches.
- NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences140
This work proposes a hierarchical network named NM-Net with a series of graph convolutions that is insensitive to the order of correspondences that achieves the state-of-the-art performance on four datasets with various inlier ratios and varying numbers of feature consistencies.
- The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World133
The All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding, is developed with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance.
- Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage133
A novel crops segmentation method (AP-HI) which is robust and not sensitive to the challenging variation of outdoor luminosity and complex environmental elements is proposed which can meet the demand for practical observation needed for agronomic modeling and in triggering action alerts to farmers.
- Action recognition for depth video using multi-view dynamic images130
This study extends dynamic imaging to the depth domain and proposes a novel CNN model to perform feature learning on multi-view dynamic images aimed at enhancing the tuning effectiveness on shallow convolutional layers by alleviating the gradient vanishing problem.
- 3DV: 3D Dynamic Voxel for Action Recognition in Depth Video127
3D dynamic voxel (3DV) is proposed as a novel 3D motion representation manner that is lightweight and effective for deep feature learning towards point set and split the depth video into temporal segments and encoded in 3DV integrally.
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- MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo125
A new generalizable 3D Gaussian representation approach derived from Multi-View Stereo that can efficiently reconstruct unseen scenes with convincing generalizability, real-time rendering speed, and fast per-scene optimization.
- Crop segmentation from images by morphology modeling in the CIE L*a*b* color space117
A new morphology modeling method is utilized to establish the crop color model in the CIE L^*a^*b^* (or Lab for simplification) color space and to realize the crop image segmentation, demonstrating that this method is robust to the variation of illumination in the field and performed better than eight other approaches.
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- Point-Query Quadtree for Crowd Counting, Localization, and More110
For the first time, a single counting model can address multiple crowd-related tasks across different learning paradigms and is implemented via data-dependent quadtree, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions.
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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 2026-10-12. 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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