FigureSeer: Parsing Result-Figures in Research Papers

@inproceedings{Siegel2016FigureSeerPR,
  title={FigureSeer: Parsing Result-Figures in Research Papers},
  author={Noah Siegel and Zachary Horvitz and Roie Levin and S. Divvala and Ali Farhadi},
  booktitle={ECCV},
  year={2016}
}
‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall. [...] Key Method The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach.Expand
64 Citations
Diag2graph: Representing Deep Learning Diagrams In Research Papers As Knowledge Graphs
  • 2
FigureQA: An Annotated Figure Dataset for Visual Reasoning
  • 67
  • PDF
Extracting Scientific Figures with Distantly Supervised Neural Networks
  • 38
  • PDF
Figure Captioning with Relation Maps for Reasoning
  • 2
  • PDF
Figure Captioning with Reasoning and Sequence-Level Training
  • 10
  • Highly Influenced
  • PDF
LEAF-QA: Locate, Encode & Attend for Figure Question Answering
  • 9
  • PDF
DocFigure: A Dataset for Scientific Document Figure Classification
  • 3
  • Highly Influenced
Neural caption generation over figures
  • 3
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 58 REFERENCES
Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers
  • 59
  • PDF
The Pascal Visual Object Classes Challenge: A Retrospective
  • 2,806
  • PDF
Mind's eye: A recurrent visual representation for image caption generation
  • 391
  • PDF
Improving state-of-the-art OCR through high-precision document-specific modeling
  • 25
  • PDF
ImageNet: A large-scale hierarchical image database
  • 16,669
  • PDF
...
1
2
3
4
5
...