Commonly Uncommon: Semantic Sparsity in Situation Recognition

@article{Yatskar2017CommonlyUS,
  title={Commonly Uncommon: Semantic Sparsity in Situation Recognition},
  author={Mark Yatskar and Vicente Ordonez and Luke S. Zettlemoyer and Ali Farhadi},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6335-6344}
}
Semantic sparsity is a common challenge in structured visual classification problems, when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is happening in images, including activities, objects and the roles objects play within the activity. For this problem, we find empirically that most substructures required… CONTINUE READING
Related Discussions
This paper has been referenced on Twitter 13 times. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-7 of 7 extracted citations

Automatic generation of composite image descriptions

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) • 2017
View 8 Excerpts
Highly Influenced

Recurrent Models for Situation Recognition

2017 IEEE International Conference on Computer Vision (ICCV) • 2017
View 9 Excerpts
Highly Influenced

Situation Recognition with Graph Neural Networks

2017 IEEE International Conference on Computer Vision (ICCV) • 2017
View 6 Excerpts
Highly Influenced

References

Publications referenced by this paper.
Showing 1-10 of 46 references

Compact Bilinear Pooling

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 1 Excerpt

Grounded Semantic Role Labeling

HLT-NAACL • 2016
View 1 Excerpt

Neural Module Networks

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 1 Excerpt

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 10 Excerpts

Bilinear CNN Models for Fine-Grained Visual Recognition

2015 IEEE International Conference on Computer Vision (ICCV) • 2015
View 1 Excerpt

Deep Visual-Semantic Alignments for Generating Image Descriptions

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2015
View 1 Excerpt

Similar Papers

Loading similar papers…