Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate queryfocused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.
Center for Research in Computer Vision at UCF（中佛罗里达大学计算机视觉研究中心）
Video data is explosively growing. As a result of the "big video data", intelligent algorithms for automatic video summarization have re-emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot's relevance to the user query and importance in the context of the video, jointly. We verify our appro…
Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest. Video summarization aims at providing such a technique by transforming one or multiple videos into a compact one. However, conventional multi-video summarization methods often fail to produce satisfying results as they ignore the user's search intent. To this end, this paper proposes a novel query-aware approach by formulating the multi-video summarization in a sparse coding framework, where the web images searched by the query a…
UiT The Arctic University of Norway（挪威特罗姆瑟大学（北极大学））；Carnegie Mellon University（卡内基梅隆大学）
Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discri…
The target of automatic video summarization is to create a short skim of the original long video while preserving the major content/events. There is a growing interest in the integration of user queries into video summarization or query-driven video summarization. This video summarization method predicts a concise synopsis of the original video based on the user query, which is commonly represented by the input text. However, two inherent problems exist in this query-driven way. First, the text query might not be enough to describe the exact and diverse needs of the user. Second, the user cann…
University of Central Florida（中佛罗里达大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences …
Video summarization plays an important role in selecting keyframe for understanding a video. Traditionally, it aims to find the most representative and diverse contents (or frames) in a video for short summaries. Recently, query-conditioned video summarization has been introduced, which considers user queries to learn more user-oriented summaries and its preference. However, there are obstacles in text queries for user subjectivity and finding similarity between the user query and input frames. In this work, (i) Image is introduced as a query for user preference (ii) a mathematical model is pr…
When video collections become huge, how to explore both within and across videos efficiently is challenging. Video summarization is one of the ways to tackle this issue. Traditional summarization approaches limit the effectiveness of video exploration because they only generate one fixed video summary for a given input video independent of the information need of the user. In this work, we introduce a method which takes a text-based query as input and generates a video summary corresponding to it. We do so by modeling video summarization as a supervised learning problem and propose an end-to-e…
The growing rate of public space CCTV installations has generated a need for automated methods for exploiting video surveillance data including scene understanding, query, behaviour annotation and summarization. For this reason, extensive research has been performed on surveillance scene understanding and analysis. However, most studies have considered single scenes, or groups of adjacent scenes. The semantic similarity between different but related scenes (e.g., many different traffic scenes of similar layout) is not generally exploited to improve any automated surveillance tasks and reduce m…
Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied. We address this problem by posing query-relevant summarization as a video frame subset selection problem, which lets us optimise for summaries which are simultaneously diverse, representative of the entire video, and relevant to a text query. We quantify relevance by measuring the distance between frames and queries in a common textual-visual semantic embedding space induced by a n…
Department of Computer Science, Indian Institute of Technology Bombay（印度理工学院孟买分校计算机科学系）；Department of Computer Science, University of Texas at Dallas（得克萨斯大学达拉斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of Washington, Seattle（华盛顿大学西雅图分校电气与计算机工程系）；School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai（塔塔基础研究所孟买技术与计算机科学学院）
We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks. While past work generally treats these problems differently ({\em e.g.}, different models are often used for generic and query-focused summarization), the submodular information measures allow us to study each of these problems via a unified approach. We first show that several previous query-focused and update summarization techniques have, unknowingly, used various instantiations of the aforesaid submodular information measures, providing evidence for th…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions…
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function. However, softmax function suffers in retaining high-rank representations for complex visual or sequential information, which is known as the Softmax Bottleneck problem. In this paper, we propose a novel framework named Dual Mixture Attention (DMASum) model with Meta Learning for video summarization that tackles the softmax bottleneck problem, where the Mixture of Attention layer (MoA) effectively increases the…
A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subjective, and users should have the option of customizing the summary by using natural language to specify what is important to them. Further, existing models for fully automatic generic summarization have not exploited available language models, which can serve as an effective prior for saliency. This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization, typical…
IIT Bombay（印度理工学院孟买分校）；Microsoft Corporation（微软公司）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；AitoeLabs（艾托实验室）
This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the level of entities like objects, scenes, humans and faces in the video). We investigate several summarization models which capture notions of diversity, coverage, representation and importance, and argue the utility of these different models depending on the application. While most of the prior work on submodular summarization approaches has focused oncombining several models an…
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Microsoft Corporation（微软公司）
This paper addresses automatic summarization and search in visual data comprising of videos, live streams and image collections in a unified manner. In particular, we propose a framework for multi-faceted summarization which extracts key-frames (image summaries), skims (video summaries) and entity summaries (summarization at the level of entities like objects, scenes, humans and faces in the video). The user can either view these as extractive summarization, or query focused summarization. Our approach first pre-processes the video or image collection once, to extract all important visual feat…
University of Amsterdam（阿姆斯特丹大学）；BBC Research and Development（BBC研发部）
Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding the text-based query and the video effectively are both important for the task of multi-modal video summarization…
In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the concepts from the images and extend it to videos and introduce a clustering and querying approach for sample efficiency, motivated by the recent advances in perceiver-based architectures. Our work provides further evidence that an approach with richer input context with relevant entities and actions from the videos and using these as prompts could enh…
