There are numerous document editing tools on the market, but only some are compatible with all file types. Some tools are, on the contrary, versatile yet burdensome to work with. DocHub provides the solution to these hassles with its cloud-based editor. It offers robust functionalities that allow you to complete your document management tasks efficiently. If you need to quickly Embed cross in PAP, DocHub is the ideal option for you!
Our process is incredibly straightforward: you import your PAP file to our editor → it automatically transforms it to an editable format → you make all essential changes and professionally update it. You only need a couple of moments to get your paperwork ready.
As soon as all modifications are applied, you can transform your paperwork into a reusable template. You only need to go to our editor’s left-side Menu and click on Actions → Convert to Template. You’ll find your paperwork stored in a separate folder in your Dashboard, saving you time the next time you need the same template. Try DocHub today!
hi everyone im joining from georgia institute of technology today im going to discuss cross-model joint embedding with diverse semantics we already open source our work on github so what is a cross model retrieval cross model retrieval is a retrieval task across different modalities such as image test cross model retrieval here we can we take the cross model retrieval between the cooking recipes and the food images as an example as shown in this figure given a recipe query we want to find the top match for the images similarly given a food image the cooking recipes with the highest relevance ranking are retrieved and the matched one is ranked as top one here the benchmark dataset used in this work is recipe 1 million dataset a recipe example and the two associated food images are listed below we can see that a cooking recipe consists of a title list of ingredients and the cooking instructions following pre-processing in existing works duplicated recipes recipes without images are fil