In this paper, we propose a deep neural network based approach for the group-level emotion recognition in 6th Emotion Recognition in the Wild Challenge (EmotiW 2018). The task of this challenge is to classify a group’s perceived emotion as Positive, Neutral or Negative. Like the most of current researchers on visual emotion recognition, we mainly focus on facial, scene and body clues in images. We treat each clue as mono-model feature and apply early fusion method to combine them together. Experimental results show that our proposed method has outperformed the baseline techniques with the overall test accuracy of 62.90%.
This paper proposes an image detection model to detect and classify supermarkets shelves’ commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.
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