Disentangling Intertemporal Substitution and Risk Aversion under the Expected Utility Theorem

A disturbing feature of the conventional objective function for intertemporal decisions under uncertainty is that the agent's attitudes toward intertemporal substitution and risk aversion are entangled. This paper shows that, in contrast to common perception, the two attitudes can be completely disentangled under the expected utility theorem (EUT) by modeling each of them successively in two steps. The conventional form is nested as a special case where the functions describing the two attitudes are identical. The proposed framework requires only the standard axioms of the EUT, in addition to a regulatory assumption. It is flexible in accommodating different combinations of the two attitudes, indifferent to the timing of resolution of uncertainty, intuitive to interpret, and extendable to multiple goods. The objective function under the proposed framework is time inconsistent according to Strotz's (1955) definition. I argue that Strotz's notion of time consistency is misguided. It is constructed based on a priori assumption that the agent should continuously forget history as time progresses. But this means the agent is either chronically amnesiac or self-contradictory. To be truly consistent, the agent should have one and only one objective function, determined at birth, throughout his entire life. As history unfolds, the agent updates his information set, but not his objective function

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

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 in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method

Improving Sequential Determinantal Point Processes for Supervised Video Summarization

It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. This paper is in the vein of supervised video summarization using sequential determinantal point process (SeqDPP), which models diversity by a probabilistic distribution. We improve this model in two folds. In terms of learning, we propose a large-margin algorithm to address the exposure bias problem in SeqDPP. In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary. Moreover, we also significantly extend a popular video summarization dataset by 1) more egocentric videos, 2) dense user annotations, and 3) a refined evaluation scheme. We conduct extensive experiments on this dataset (about 60 hours of videos in total) and compare our approach to several competitive baselines