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Works12 from public data
- 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.
- 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.
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- Weighing Counts: Sequential Crowd Counting by Reinforcement Learning85
Inspired by scale weighing, this work proposes a novel 'counting scale' termed LibraNet where the count value is analogized by weight and shows that Libra net exactly implements scale weighing by visualizing the decision process howLibraNet chooses actions.
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- Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images76
Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.
- Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction41
This work proposes an end-to-end network with a graph convolution module that achieves not only state-of-the-art results for depth completion of indoor scenes but also better generalization ability than other competing methods.
- From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting22
The idea of spatial divide-and-conquer (S-DC) that transforms open-set counting into a closed set problem is introduced and implemented by a novel Supervised Spatial Divide-and-Conquer Network (SS-DCNet).
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- Deep Imbalanced Regression via Hierarchical Classification Adjustment12
This work proposes a range-preserving distillation process that effectively learns a single classifier from the set of hierarchical classifiers to improve regression performance over the entire range of data.
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Publication data from OpenAlex; 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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