AI Case Study

"For the image query, object and sentiment detection are used to
extract appropriate nouns, such as city and street, and adjectives,
such as busy, as initial keyword set.... We propose detecting objects and sentiments from each image with two parallel convolutional neural networks (CNN), which share the same network architecture but with different parameters. Specifically, one network learns to describe objects by the output of noun words, and the other learns to understand the sentiments by the output of adjective words. The two CNNs are pre-trained on ImageNet and fine-tuned on noun and adjective categories, respectively.

After filtering out words with low confidence and rare words, keyword expansion will be applied to construct a keyword set. Next, each keyword is regarded as an initial seed for each sentence in the poem generation process. For example, the first sentence is generated from the seed city. A hierarchical recurrent neural network is proposed for modifying the structure between words and between sentences. We follow the recurrent neural network language model (RNNLM) to predict text sequence. Each word is predicted sequentially by the previous word sequence...

Finally we apply a fluency checker to automatically detect low quality sentences early on and re-generate them. We use Long-Short Term Memory (LSTM) for RNN... In the poetry generation model, the recurrent hidden layers for the sentence level and poem level both contain 3 layers and 1024 hidden units for each layer. The sentence encoder dimensionality is 64."