Instructional videos are very useful for completing complex daily tasks,which naturally contain abundant clip-narration pairs.Existing works for procedure understanding are keen on pretraining various video-language m...Instructional videos are very useful for completing complex daily tasks,which naturally contain abundant clip-narration pairs.Existing works for procedure understanding are keen on pretraining various video-language models with these pairs and then finetuning downstream classifiers and localizers in predetermined category space.These video-language models are proficient at representing short-term actions,basic objects,and their combinations,but they are still far from understanding long-term procedures.In addition,the predetermined procedure category faces the problem of combination disaster and is inherently inapt to unseen procedures.Therefore,we propose a novel compositional prompt learning(CPL)framework to understand long-term procedures by prompting short-term video-language models and reformulating several classical procedure understanding tasks into general video-text matching problems.Specifically,the proposed CPL consists of one visual prompt and three compositional textual prompts(including the action prompt,object prompt,and procedure prompt),which could compositionally distill knowledge from short-term video-language models to facilitate long-term procedure understanding.Besides,the task reformulation enables our CPL to perform well in all zero-shot,few-shot,and fully-supervised settings.Extensive experiments on two widely-used datasets for procedure understanding demonstrate the effectiveness of the proposed approach.展开更多
The traditional strategy of 3D model reconstruction mainly concentrates on orthographic projections or engineering drawings. But there are some shortcomings. Such as, only few kinds of solids can be reconstructed, the...The traditional strategy of 3D model reconstruction mainly concentrates on orthographic projections or engineering drawings. But there are some shortcomings. Such as, only few kinds of solids can be reconstructed, the high complexity of time and less information about the 3D model. The research is extended and process card is treated as part of the 3D reconstruction. A set of process data is a superset of 2D engineering drawings set. The set comprises process drawings and process steps, and shows a sequencing and asymptotic course that a part is made from roughcast blank to final product. According to these characteristics, the object to be reconstructed is translated from the complicated engineering drawings into a series of much simpler process drawings. With the plentiful process information added for reconstruction, the disturbances such as irrelevant graph, symbol and label, etc. can be avoided. And more, the form change of both neighbor process drawings is so little that the engineering drawings interpretation has no difficulty; in addition, the abnormal solution and multi-solution can be avoided during reconstruction, and the problems of being applicable to more objects is solved ultimately. Therefore, the utility method for 3D reconstruction model will be possible. On the other hand, the feature information in process cards is provided for reconstruction model. Focusing on process cards, the feasibility and requirements of Working Procedure Model reconstruction is analyzed, and the method to apply and implement the Natural Language Understanding into the 3D reconstruction is studied. The method of asymptotic approximation product was proposed, by which a 3D process model can be constructed automatically and intelligently. The process model not only includes the information about parts characters, but also can deliver the information of design, process and engineering to the downstream applications.展开更多
文摘Instructional videos are very useful for completing complex daily tasks,which naturally contain abundant clip-narration pairs.Existing works for procedure understanding are keen on pretraining various video-language models with these pairs and then finetuning downstream classifiers and localizers in predetermined category space.These video-language models are proficient at representing short-term actions,basic objects,and their combinations,but they are still far from understanding long-term procedures.In addition,the predetermined procedure category faces the problem of combination disaster and is inherently inapt to unseen procedures.Therefore,we propose a novel compositional prompt learning(CPL)framework to understand long-term procedures by prompting short-term video-language models and reformulating several classical procedure understanding tasks into general video-text matching problems.Specifically,the proposed CPL consists of one visual prompt and three compositional textual prompts(including the action prompt,object prompt,and procedure prompt),which could compositionally distill knowledge from short-term video-language models to facilitate long-term procedure understanding.Besides,the task reformulation enables our CPL to perform well in all zero-shot,few-shot,and fully-supervised settings.Extensive experiments on two widely-used datasets for procedure understanding demonstrate the effectiveness of the proposed approach.
文摘The traditional strategy of 3D model reconstruction mainly concentrates on orthographic projections or engineering drawings. But there are some shortcomings. Such as, only few kinds of solids can be reconstructed, the high complexity of time and less information about the 3D model. The research is extended and process card is treated as part of the 3D reconstruction. A set of process data is a superset of 2D engineering drawings set. The set comprises process drawings and process steps, and shows a sequencing and asymptotic course that a part is made from roughcast blank to final product. According to these characteristics, the object to be reconstructed is translated from the complicated engineering drawings into a series of much simpler process drawings. With the plentiful process information added for reconstruction, the disturbances such as irrelevant graph, symbol and label, etc. can be avoided. And more, the form change of both neighbor process drawings is so little that the engineering drawings interpretation has no difficulty; in addition, the abnormal solution and multi-solution can be avoided during reconstruction, and the problems of being applicable to more objects is solved ultimately. Therefore, the utility method for 3D reconstruction model will be possible. On the other hand, the feature information in process cards is provided for reconstruction model. Focusing on process cards, the feasibility and requirements of Working Procedure Model reconstruction is analyzed, and the method to apply and implement the Natural Language Understanding into the 3D reconstruction is studied. The method of asymptotic approximation product was proposed, by which a 3D process model can be constructed automatically and intelligently. The process model not only includes the information about parts characters, but also can deliver the information of design, process and engineering to the downstream applications.
