Summarizing Videos with Attention

In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-attention based network for video summarization which performs the entire sequence to sequence transformation in a single feed forward pass and single backward pass during training. Our method sets a new state of the art results on two benchmarks TvSum and SumMe, commonly used in this domain.Comment: Presented at ACCV2018 AIU2018 worksho

Summarizing First-Person Videos from Third Persons' Points of Views

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small number of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.Comment: 16+10 pages, ECCV 201

Collaborative Summarization of Topic-Related Videos

Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.Comment: CVPR 201

Learning Latent Super-Events to Detect Multiple Activities in Videos

In this paper, we introduce the concept of learning latent super-events from activity videos, and present how it benefits activity detection in continuous videos. We define a super-event as a set of multiple events occurring together in videos with a particular temporal organization; it is the opposite concept of sub-events. Real-world videos contain multiple activities and are rarely segmented (e.g., surveillance videos), and learning latent super-events allows the model to capture how the events are temporally related in videos. We design temporal structure filters that enable the model to focus on particular sub-intervals of the videos, and use them together with a soft attention mechanism to learn representations of latent super-events. Super-event representations are combined with per-frame or per-segment CNNs to provide frame-level annotations. Our approach is designed to be fully differentiable, enabling end-to-end learning of latent super-event representations jointly with the activity detector using them. Our experiments with multiple public video datasets confirm that the proposed concept of latent super-event learning significantly benefits activity detection, advancing the state-of-the-arts.Comment: CVPR 201