Corpus ID: 102354672

Comparison Network for One-Shot Conditional Object Detection

@article{Zhang2019ComparisonNF,
  title={Comparison Network for One-Shot Conditional Object Detection},
  author={T. Zhang and Y. Zhang and Xian Sun and Hao Sun and Menglong Yan and X. Yang and K. Fu},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.02317}
}
The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet… Expand
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References

SHOWING 1-10 OF 52 REFERENCES
You Only Look Once: Unified, Real-Time Object Detection
  • 11,363
  • PDF
Zero-Shot Object Detection by Hybrid Region Embedding
  • 44
  • PDF
Few-Example Object Detection with Model Communication
  • 95
  • PDF
LSTD: A Low-Shot Transfer Detector for Object Detection
  • 89
  • Highly Influential
  • PDF
Focal Loss for Dense Object Detection
  • 2,508
  • Highly Influential
  • PDF
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  • 13,417
  • PDF
High Performance Visual Tracking with Siamese Region Proposal Network
  • 677
  • Highly Influential
  • PDF
SSD: Single Shot MultiBox Detector
  • 10,642
  • PDF
Weakly Supervised Object Localization with Progressive Domain Adaptation
  • 145
  • PDF
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  • 23,303
  • Highly Influential
  • PDF
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