From a92337ed2270af9b10806c746dcb4e9fa959ffbb Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 31 Oct 2018 01:53:14 +0100 Subject: codingg --- ...ecognition: Visual Semantic Role Labeling for Image Understanding.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv (limited to 'datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv') diff --git a/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv new file mode 100644 index 00000000..503356df --- /dev/null +++ b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv @@ -0,0 +1 @@ +Situation recognition: Visual semantic role labeling for image understanding|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html|2016|53|9|14769542088507071062|None|http://scholar.google.com/scholar?cites=14769542088507071062&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14769542088507071062&hl=en&as_sdt=0,5|None|This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including:(1) the main activity (eg, clipping),(2) the participating actors, objects, substances, and locations (eg, man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (eg, the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a … -- cgit v1.2.3-70-g09d2