Deep Sentiment Features of Context and Faces for Affective Video Analysis

Given the huge quantity of hours of video available on video sharing platforms such as YouTube, Vimeo, etc. development of automatic tools that help users nd videos that t their interests has attracted the attention of both scienti c and industrial communities. So far the majority of the works have addressed semantic analysis, to identify objects, scenes and events depicted in videos, but more recently a ective analysis of videos has started to gain more at- tention. In this work we investigate the use of sentiment driven features to classify the induced sentiment of a video, i.e. the senti- ment reaction of the user. Instead of using standard computer vision features such as CNN features or SIFT features trained to recognize objects and scenes, we exploit sentiment related features such as the ones provided by Deep-SentiBank [4], and features extracted from models that exploit deep networks trained on face expressions. We experiment on two recently introduced datasets: LIRIS-ACCEDE [2] and MEDIAEVAL-2015, that provide sentiment annotations of a large set of short videos. We show that our approach not only outperforms the current state-of-the-art in terms of valence and arousal classi cation accuracy, but it also uses a smaller number of features, requiring thus less video processing

PLM-IPE: A Pixel-Landmark Mutual Enhanced Framework for Implicit Preference Estimation

In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, without any kind of active engagement, is in fact an essential block to achieve interactive applications. We propose a pixel-landmark mutual enhanced framework for implicit preference estimation, named PLM-IPE, which is capable of inferring the user's implicit preferences exploiting visual cues, without any active or conscious engagement. PLM-IPE consists of three key modules: pixel-based estimator, landmark-based estimator and mutual learning based optimization. The former two modules work on capturing the implicit reaction of the user from the pixel level and landmark level, respectively. The last module serves to transfer knowledge between the two parallel estimators. Towards evaluation, we collected a real-world dataset, named SentiGarment, which contains 3,345 facial reaction videos paired with suggested outfits and human labeled reaction scores. Extensive experiments show the superiority of our model over state-of-the-art approaches