diff options
| author | jules@lens <julescarbon@gmail.com> | 2019-02-18 13:53:18 +0100 |
|---|---|---|
| committer | jules@lens <julescarbon@gmail.com> | 2019-02-18 13:53:18 +0100 |
| commit | 3fc5bb42b0dd94b56d0f11b1568d30a1ff835629 (patch) | |
| tree | 0a56e7f9cb84bda15a6e3074d1eba8312cc058f5 /site/datasets/citations/precarious.json | |
| parent | b28f65ad5016ba3c3c9f973bd2a64ea3c8a3f84c (diff) | |
rebuild everything
Diffstat (limited to 'site/datasets/citations/precarious.json')
| -rw-r--r-- | site/datasets/citations/precarious.json | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/site/datasets/citations/precarious.json b/site/datasets/citations/precarious.json index 3834f30c..96fd92fc 100644 --- a/site/datasets/citations/precarious.json +++ b/site/datasets/citations/precarious.json @@ -1 +1 @@ -{"id": "9e5378e7b336c89735d3bb15cf67eff96f86d39a", "paper": {"paperId": "9e5378e7b336c89735d3bb15cf67eff96f86d39a", "key": "precarious", "title": "Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters", "journal": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)", "address": "", "address_type": "", "lat": "", "lng": "", "pdf_link": "https://arxiv.org/pdf/1703.06283.pdf", "report_link": "papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html", "citation_count": 12, "citations_geocoded": 1, "citations_unknown": 11, "citations_empty": 1, "citations_pdf": 10, "citations_doi": 1, "name": "Precarious"}, "address": null, "citations": [["Modeling Camera Effects to Improve Visual Learning from Synthetic Data", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", "", "2018"]]}
\ No newline at end of file +{"id": "9e5378e7b336c89735d3bb15cf67eff96f86d39a", "paper": {"paperId": "9e5378e7b336c89735d3bb15cf67eff96f86d39a", "key": "precarious", "title": "Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters", "journal": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)", "address": "", "address_type": "", "lat": "", "lng": "", "pdf_link": "https://arxiv.org/pdf/1703.06283.pdf", "report_link": "papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html", "citation_count": 12, "citations_geocoded": 3, "citations_unknown": 9, "citations_empty": 1, "citations_pdf": 10, "citations_doi": 1, "name": "Precarious"}, "address": null, "citations": [["Joint Holistic and Partial CNN for Pedestrian Detection", "", "China", "China", "China", "35.86166000", "104.19539700", "edu", "", "2018"], ["Modeling Camera Effects to Improve Visual Learning from Synthetic Data", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", "", "2018"], ["Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance", "Volvo Construction Equipment, G\u00f6thenburg, Sweden", "Volvo Construction Equipment, G\u00f6thenburg, Sweden", "Volvo Construction Equipment, G\u00f6thenburg, Sweden", "Gropeg\u00e5rdsgatan 11, 417 15 G\u00f6teborg, Sweden", "57.71720040", "11.92185580", "edu", "", "2018"]]}
\ No newline at end of file |