Understanding web instructional videos is an essential branch of video understanding in two aspects. First, most existing video methods focus on short-term actions for a-few-second-long video clips; these methods are not directly applicable to long videos. Second, unlike unconstrained long videos, e.g., movies, instructional videos are more structured in that they have step-by-step procedure constraining the understanding task. In this paper, we study reasoning on instructional videos via question-answering (QA). Surprisingly, it has not been an emphasis in the video community despite its rich applications. We thereby introduce YouQuek, an annotated QA dataset for instructional videos based on the recent YouCook2. The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension. Observing the lack of effective representations for modeling long videos, we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network (RGCN) that captures both temporal order and relation information. Furthermore, we study multiple modalities including description and transcripts for the purpose of boosting video understanding. Extensive experiments on YouQuek suggest that RGCN performs the best in terms of QA accuracy and a better performance is gained by introducing human annotated description.
People are gaining interests in video question answering (VideoQA) in recent years. Most of the current VideoQA tasks are focusing on direct facts in short videos @cite_10 @cite_21 @cite_3 @cite_22 @cite_11 . They all automatically generate QA pairs using a state-of-the-art question generation algorithm proposed in @cite_29 . However, such auto-generation mechanism often generates QA pairs with poor quality and low diversity, though grammatically correct. Worse still, auto-generated QA pairs cannot involve reasoning. From the reasoning point of view, MovieQA @cite_31 use human annotated QA pairs on movies to evaluate automatic story comprehension. SVQA @cite_23 , following the step of @cite_2 , extend the CLEVR dataset to the video version. Yet, it still focuses on short-term relations, and does not fit natural settings.
@cite_22 @cite_29 @cite_21 @cite_3 @cite_23 @cite_2 @cite_31 @cite_10 @cite_11
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Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark. Video question answering (VideoQA) always involves visual reasoning. When answering questions composing of multiple logic correlations, models need to perform multi-step reasoning. In this paper, we formulate multi-step reasoning in VideoQA as a new task to answer compositional and logical structured questions based on video content. Existing VideoQA datasets are inadequate as benchmarks for the multi-step reasoning due to limitations such as lacking logical structure and having language biases. Thus we design a system to automatically generate a large-scale dataset, namely SVQA (Synthetic Video Question Answering). Compared with other VideoQA datasets, SVQA contains exclusively long and structured questions with various spatial and temporal relations between objects. More importantly, questions in SVQA can be decomposed into human readable logical tree or chain layouts, each node of which represents a sub-task requiring a reasoning operation such as comparison or arithmetic. Towards automatic question answering in SVQA, we develop a new VideoQA model. Particularly, we construct a new attention module, which contains spatial attention mechanism to address crucial and multiple logical sub-tasks embedded in questions, as well as a refined GRU called ta-GRU (temporal-attention GRU) to capture the long-term temporal dependency and gather complete visual cues. Experimental results show the capability of multi-step reasoning of SVQA and the effectiveness of our model when compared with other existing models. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.