Episodic event memory is a key component of human cognition. Predicting event memorability,i.e., to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months. Subjects’ memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-of-its-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks. The findings inform deeper understanding of episodic event memory, and open up a new avenue for prediction of human episodic memory. Source code is available at https://github.com/ffzzy840304/Predicting-Event-Memorability.

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping ( SRM ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.

Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments. However, this "interestingness" of a video segment or image is subjective. Thus, such highlight models provide results of limited relevance for the individual user. On the other hand, training one model per user is inefficient and requires large amounts of personal information which is typically not available. To overcome these limitations, we present a global ranking model which can condition on a particular user's interests. Rather than training one model per user, our model is personalized via its inputs, which allows it to effectively adapt its predictions, given only a few user-specific examples. To train this model, we create a large-scale dataset of users and the GIFs they created, giving us an accurate indication of their interests. Our experiments show that using the user history substantially improves the prediction accuracy. On a test set of 850 videos, our model improves the recall by 8% with respect to generic highlight detectors. Furthermore, our method proves more precise than the user-agnostic baselines even with only one single person-specific example.

Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way. However, such methodologies are ineffective to deal with heterogeneous events, e.g. taking a walk, and temporary changes in the sight direction, e.g. at a meeting. To address these limitations, we propose Contextual Event Segmentation (CES), a novel segmentation paradigm that uses an LSTM-based generative network to model the photo-stream sequences, predict their visual context, and track their evolution. CES decides whether a frame is an event boundary by comparing the visual context generated from the frames in the past, to the visual context predicted from the future. We implemented CES on a new and massive lifelogging dataset consisting of more than 1.5 million images spanning over 1,723 days. Experiments on the popular EDUB-Seg dataset show that our model outperforms the state-of-the-art by over 16% in f-measure. Furthermore, CES' performance is only 3 points below that of human annotators.

To facilitate the browsing of long videos, automatic video summarization provides an excerpt that represents its content. In the case of egocentric and consumer videos, due to their personal nature, adapting the summary to specific user's preferences is desirable. Current approaches to customizable video summarization obtain the user's preferences prior to the summarization process. As a result, the user needs to manually modify the summary to further meet the preferences. In this paper, we introduce Active Video Summarization (AVS), an interactive approach to gather the user's preferences while creating the summary. AVS asks questions about the summary to update it on-line until the user is satisfied. To minimize the interaction, the best segment to inquire next is inferred from the previous feedback. We evaluate AVS in the commonly used UTEgo dataset. We also introduce a new dataset for customized video summarization (CSumm) recorded with a Google Glass. The results show that AVS achieves an excellent compromise between usability and quality. In 41% of the videos, AVS is considered the best over all tested baselines, including summaries manually generated. Also, when looking for specific events in the video, AVS provides an average level of satisfaction higher than those of all other baselines after only six questions to the user.

The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets suitable for summarization and, then, the various evaluation methods. Finally, we analyze the challenges and opportunities in the field and propose new lines of research.

Our life is becoming heavily documented and expressed on the digital substrate. This booming flow of consumer video has lead to an increasing demand of multimedia analysis tools to organize and summarize those visual memories. Due to the personal nature of such videos, though, the summarization needs to be adapted to the user needs and preferences. Yet, most summarization systems rely solely on pre-defined criteria, e.g. story-coherence or interestingness pre-trained classifiers. I propose a system which is capable of finding relevant digital memories to a given semantic query, and then summarize them on a customized manner. The proposed framework includes a wide set of tools to match a user's needs, from retrieval using multimodal queries to summarization striving to his/her preferences, both provided passively and actively. Preliminary results show the high potential of such a framework, with over 70% retrieval accuracy. More importantly, as seen from the user study, the summaries generated achieve an unprecedented compromise between usability and quality.