Video question answering(Video QA)involves a thorough understanding of video content and question language,as well as the grounding of the textual semantic to the visual content of videos.Thus,to answer the questions more accurately,not only the semantic entity should be associated with certain visual instance in video frames,but also the action or event in the question should be localized to a corresponding temporal slot.It turns out to be a more challen-ging task that requires the ability of conducting reasoning with correlations between instances along temporal frames.In this paper,we propose an instance-sequence reasoning network for video question answering with instance grounding and temporal localization.In our model,both visual instances and textual representations are firstly embedded into graph nodes,which benefits the integration of intra-and inter-modality.Then,we propose graph causal convolution(GCC)on graph-structured sequence with a large receptive field to capture more causal connections,which is vital for visual grounding and instance-sequence reasoning.Finally,we evaluate our model on TVQA+dataset,which contains the groundtruth of instance grounding and temporal localization,three other Video QA datasets and three multimodal language processing datasets.Extensive experiments demonstrate the effectiveness and generalization of the proposed method.Specifically,our method outperforms the state-of-the-art methods on these benchmarks.

Rui LIU;Yahong HAN

College of Intelligence and Computing,Tianjin University,Tianjin 300350,China;Tianjin Key Lab of Machine Learning,Tianjin University,Tianjin 300350,China

计算机科学前沿

[1]Rui LIU;Yahong HAN-.Instance-sequence reasoning for video question answering)[J].计算机科学前沿,2022(06):89-97

TVQA+dataset,groundtruth

Instance,sequence,reasoning,answering,Video,involves,thorough,understanding,content,language,well,grounding,textual,semantic,visual,videos,Thus,questions,more,accurately,not,only,entity,should,associated,certain,frames,but,also,action,event,localized,corresponding,temporal,slot,It,turns,challen,ging,task,that,requires,ability,conducting,correlations,between,instances,along,this,paper,network,localization,our,model,both,representations,are,firstly,embedded,into,graph,nodes,which,benefits,integration,intra,inter,modality,Then,causal,convolution,GCC,structured,large,receptive,field,capture,connections,vital,Finally,evaluate,contains,three,other,datasets,multimodal,processing,Extensive,experiments,demonstrate,effectiveness,generalization,proposed,Specifically,outperforms,state,art,methods,these,benchmarks

0.504363