Story-Driven Summarization for Egocentric Video

By Zheng Lu, Kristen Grauman. 2013

542 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Summarization, Signal Processing

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Citations in the corpus

Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, background): “Egocentric video understanding Egocentric video offers a host of interesting challenges, such as human-object interactions [26, 46, 163], activity recognition [110, 139, 243], anticipation [4, 75, 86, 144, 205], video summarization [48, 129, 131, 147, 148, 232], detecting hands [16, 134], parsing so”
Unsupervised Semantic Parsing of Video Collections (2015, methodology): “Summarizing videos is particularly important for long sequences like ego-centric videos and news reports [35, 38, 50]; however, these methods mostly rely on characteristics of the application and do not generalize.”
Diverse Sequential Subset Selection for Supervised Video Summarization (2014, background): “Recent developments tackle summarization for dynamic cameras that are worn or handheld [10, 8, 9] or design online algorithms to process streaming data [7].”
Category-Specific Video Summarization (2014, unknown): “A few previous works [3,6,5] produced summaries using features crafted for specific visual categories.”
This Hand Is My Hand: A Probabilistic Approach to Hand Disambiguation in Egocentric Video (2014, background): “Egocentric video is becoming a popular research topic in computer vision, with papers recognizing activities [7, 8, 19, 22], events [15, 18, 25], and objects [9, 21].”
Textually Customized Video Summaries (2017, background): “[21] develop a model considering the relationship of inﬂuential objects contributing to event progression.”
Summarization of Egocentric Videos: A Comprehensive Survey (2017, methodology): “Finally, importance may be estimated with supervised learning [23], [25], [26]; inferred from impersonal cues such as saliency [17], people and object interaction [10], [12], [16], [17], [19], and location [13]; or predicted from the wearer attention patterns, using sensors [14], [15], [22] or motio”
Enhancing Video Summarization via Vision-Language Embedding (2017, methodology): “Consequently, many methods for computing video summaries have been proposed by researchers [1, 5, 6, 8, 15, 20, 25, 30, 42, 48, 47].”
You-Do, I-Learn: Discovering Task Relevant Objects and their Modes of Interaction from Multi-User Egocentric Video (2014, background): “Recently, a shift towards automatic discovery of objects for video summaries [25] or action recognition [9] has been witnessed, to which this work contributes.”
Detecting Engagement in Egocentric Video (2016, methodology): “Recent methods explore ways to predict the “importance” of spatial regions (objects, people) using cues like hand detection and frame centrality [28], [29], detect novelty [30], and infer “social saliency” when multiple cameras capture the same event [31]–[33].”
Recognizing Micro-Actions and Reactions from Paired Egocentric Videos (2016, background): “First-person vision is one of the emerging topics in computer vision, which greatly affects several applications such as automatic activity summarization [4, 15, 20, 43] and assistive systems [16, 34, 35, 36].”
DevNet: A Deep Event Network for multimedia event detection and evidence recounting (2015, background): “It is unlike traditional video summarization tasks [24, 27, 12], which mainly seek to reduce the redundancy of the videos.”
Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition (2019, background): “, frame sampling [2], video summarization [3], visual recogni-”
KrishnaCam: Using a longitudinal, single-person, egocentric dataset for scene understanding tasks (2016, background): “The two peaks of the graph (steep rises in the amount of novel visual data) correspond to days the student spent outside his home city.”
Computer Vision and Image Understanding Computer Vision for Assistive Technologies (unknown): “…and temporal segmentation of daily actions and contexts in video ( Damen et al., 2014; Furnari et al., 2015; 2016; Gurrin et al., 2014; Lee et al., 2012; Lee and Grauman, 2015; Lu and Grauman, 2013; Ortis et al., 2016; Poleg et al., 2014 ) can greatly help to support users in their daily life.”
TVSum: Summarizing web videos using titles (2015, background): “, salient objects in sports [17], and salient regions in egocentric videos [27, 32].”
Predicting Important Objects for Egocentric Video Summarization (2015, methodology): “ual summary, and model high-level importance of the objects present. To our knowledge, we are the ﬁrst to explore visual summarization of egocentric video by predicting important objects. Recent work [46] builds on our approach and uses our importance predictions as a cue to generate story-driven eg”
Revealing Event Saliency in Unconstrained Video Collection (2017, unknown): “Event saliency also differs from the task of video summarization [6], [7] as the latter mainly aims at extracting video fragments to help users obtaining quick comprehension of the whole story (including the beginning, the key progresses of the story, and the end) of the given single video, whereas…”
Multi-task deep visual-semantic embedding for video thumbnail selection (2015, methodology): “propose a saliency based video summarization method, which trains a linear regression model to predict the importance score for each frame in egocentric videos [12].”