We propose a novel problem revolving around two tasks:(i)given a scene,recommend objects to insert,and(ii)given an object category,retrieve suitable background scenes.A bounding box for the inserted object is predicte...We propose a novel problem revolving around two tasks:(i)given a scene,recommend objects to insert,and(ii)given an object category,retrieve suitable background scenes.A bounding box for the inserted object is predicted in both tasks,which helps downstream applications such as semiautomated advertising and video composition.The major challenge lies in the fact that the target object is neither present nor localized in the input,and furthermore,available datasets only provide scenes with existing objects.To tackle this problem,we build an unsupervised algorithm based on object-level contexts,which explicitly models the joint probability distribution of object categories and bounding boxes using a Gaussian mixture model.Experiments on our own annotated test set demonstrate that our system outperforms existing baselines on all sub-tasks,and does so using a unified framework.Future extensions and applications are suggested.展开更多
基金supported by the National Key Technology R&D Program(Project Number 2016YFB1001402)the National Natural Science Foundation of China(Project Numbers61521002,61772298)+1 种基金Research Grant of Beijing Higher Institution Engineering Research CenterTsinghua–Tencent Joint Laboratory for Internet Innovation Technology.
文摘We propose a novel problem revolving around two tasks:(i)given a scene,recommend objects to insert,and(ii)given an object category,retrieve suitable background scenes.A bounding box for the inserted object is predicted in both tasks,which helps downstream applications such as semiautomated advertising and video composition.The major challenge lies in the fact that the target object is neither present nor localized in the input,and furthermore,available datasets only provide scenes with existing objects.To tackle this problem,we build an unsupervised algorithm based on object-level contexts,which explicitly models the joint probability distribution of object categories and bounding boxes using a Gaussian mixture model.Experiments on our own annotated test set demonstrate that our system outperforms existing baselines on all sub-tasks,and does so using a unified framework.Future extensions and applications are suggested.
