{"arxiv_id":"2404.00373","incoming":{"papers":1,"sentences":1,"groups":[],"unlabelled_sentences":1},"outgoing":{"papers":3,"sentences":5,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":1,"sentences":2,"quotes":[{"arxiv_id":"2302.05543","title":"Adding Conditional Control to Text-to-Image Diffusion Models","url":"/paper/2302.05543","section":"Introduction","sentence":"Furthermore, as shown in the yellow box, we utilize ControlNet's [ref] edge control conditions and stable diffusion models [ref] to generate a series of images with diverse styles from the edge map.","intent_p":0.98,"contests_p":null}]},{"kind":"data_used","label":"Uses data or values from","papers":1,"sentences":1,"quotes":[{"arxiv_id":"1908.00463","title":"DIODE: A Dense Indoor and Outdoor DEpth Dataset","url":"/paper/1908.00463","section":"Datasets and Metrics","sentence":"We evaluate our method in three commonly used datasets, DIODE [ref] , IBims-1 [ref] , and NYU-v2 [ref] .","intent_p":0.93,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2212.05315","title":"Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation","url":"/paper/2212.05315","section":"Related Works","sentence":"Talker et al. [ref] introduce a depth edges loss to enforce sharp edges in the estimated depth maps.","intent_p":0.94,"contests_p":null}]}],"unlabelled_sentences":1},"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/2404.00373"}}