Added datasets and results for emotion and activity recognition

are_we_there_yet/data/curated_data.yml

@@ -1600,3 +1600,23 @@
   :result: 1.01%
   :paper_pdf_url: http://papers.nips.cc/paper/5647-binaryconnect-training-deep-neural-networks-with-binary-weights-during-propagations.pdf
   :additional_information: Using 50% dropout
+- :dataset_name: GIFGIF
+  :paper_name: 'Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks'
+  :paper_year: '2016'
+  :result: 0:6652
+  :paper_pdf_url: http://affect.media.mit.edu/pdfs/16.Chen-etal-ISM-full.pdf
+- :dataset_name: Video Emotion Dataset
+  :paper_name: 'Emotion in Context: Deep Semantic Feature Fusion for Video Emotion Recognition'
+  :paper_year: '2016'
+  :result: 50.6
+  :paper_pdf_url: http://www.yugangjiang.info/publication/16MM-emotion.pdf
+- :dataset_name: Ekman Emotion Dataset
+  :paper_name: 'Emotion in Context: Deep Semantic Feature Fusion for Video Emotion Recognition'
+  :paper_year: '2016'
+  :result: 51.8
+  :paper_pdf_url: http://www.yugangjiang.info/publication/16MM-emotion.pdf
+- :dataset_name: NTU RGB+D
+  :paper_name: 'Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition'
+  :paper_year: '2016'
+  :result: 77.7
+  :paper_pdf_url: https://arxiv.org/pdf/1607.07043v1.pdf

are_we_there_yet/data/datasets.yml

@@ -38,6 +38,30 @@ Classification:
     See this interesting [comparative analysis](http://www.image-net.org/challenges/LSVRC/2012/analysis/).
   :figure_url: ilsvrc2012_task1.png
   :external_results_url: http://www.image-net.org/challenges/LSVRC/2012/results.html#t1
+- :group: Classification
+  :name: GIFGIF
+  :evaluation_units: nMSE
+  :description: |-
+    Over 6000 annotated animated GIFs with 17 emotional labels.
+  :figure_url: gifgif.png
+- :group: Classification
+  :name: Video Emotion Dataset
+  :evaluation_units: accuracy %
+  :description: |-
+    Contains 1,101 videos from YouTube and Flickr with an average duration of 107 seconds. It is manfully annotated into 8 basic emotion categories according to the Plutchik’s wheel of Emotions, including "anger", "anticipation", "disgust", "fear", "sadness", "joy", "surprise" and "trust".
+  :figure_url: video-emotion-dataset.png
+- :group: Classification
+  :name: Ekman Emotion Dataset
+  :evaluation_units: accuracy %
+  :description: |-
+    Contains 1,637 videos labeled into Ekman’s 6 basic emotions (joy, sadness, anger, fear, disgust, surprise) by 10 volunteers with a minimum number of 221 videos per category.
+  :figure_url: ekman-emotion-video.png
+- :group: Classification
+  :name: NTU RGB+D
+  :evaluation_units: accuracy %
+  :description: |-
+    This dataset consists of 56,880 action samples containing 4 different modalities (RGB videos, depth map sequences, 3D skeletal data, infrared videos) of data for each sample and [60 classes of actions](https://github.com/shahroudy/NTURGB-D).
+  :figure_url: ntu-rgbd.png
 Detection:
 - :group: Detection
   :name: Pascal VOC 2007 comp3