Instance-sequence reasoning for video question answering

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 challenging 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.

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A Systems

Performance of Video Question and Answer(VQA)systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’answers.However,traditional linear combinations of multimodal features focus only on shallow feature interactions,fall far short of the need of deep feature fusion.Attention mechanisms were used to perform deep fusion,but most of them can only process weight assignment of single-modal information,leading to attention imbalance for different modalities.To address above problems,we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion(TMCF)and Self-AdaptiveMultimodal Balancing Mechanism(SAMB).Our model is designed to enhance complex feature interactions among multimodal features with cross-modal information balancing.In addition,TMCF and SAMB can be used as an extensible plug-in for exploring new feature combinations in the visual image domain.Extensive experiments were conducted on MSVDQA and MSRVTT-QA datasets.The results confirm the advantages of our approach in handling multimodal tasks.Besides,we also provide analyses for ablation studies to verify the effectiveness of each proposed component.