arXiv subjects

Cristian Rodriguez-Opazo

Publications and source records attributed to Cristian Rodriguez-Opazo.

20 records · Page 2Linked to original sources

arXiv2020

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.

cs.CL↗

arXiv2020

Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention

This paper studies the problem of temporal moment localization in a long untrimmed video using natural language as the query. Given an untrimmed video and a sentence as the query, the goal is to determine the starting, and the ending, of the relevant visual moment in the video, that corresponds to the query sentence. While previous works have tackled this task by a propose-and-rank approach, we introduce a more efficient, end-to-end trainable, and {\em proposal-free approach} that relies on three key components: a dynamic filter to transfer language information to the visual domain, a new loss function to guide our model to attend the most relevant parts of the video, and soft labels to model annotation uncertainty. We evaluate our method on two benchmark datasets, Charades-STA and ActivityNet-Captions. Experimental results show that our approach outperforms state-of-the-art methods on both datasets.

cs.CV↗