{"arxiv_id":"2208.06117","incoming":{"papers":0,"sentences":0,"groups":[],"unlabelled_sentences":0},"outgoing":{"papers":4,"sentences":5,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":1,"sentences":1,"quotes":[{"arxiv_id":"1708.02043","title":"What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?","url":"/paper/1708.02043","section":"Image Description Generation Model","sentence":"We use the merge architecture to construct descriptions for images [ref] with the VGG16 model for extracting image features and the LSTM model for constructing image caption, as presented in Figure [ref].","intent_p":1.0,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"1708.02043","title":"What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?","url":"/paper/1708.02043","section":"Introduction","sentence":"For the last category, there are 2 architectures to generate images captions: inject and merge architectures [ref] .","intent_p":0.83,"contests_p":null}]}],"unlabelled_sentences":3},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2208.06117"}}