Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work

Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field

Mining User Personality from Music Listening Behavior in Online Platforms Using Audio Attributes

Music and emotions are inherently intertwined. Humans leave hints of their personality everywhere, and particularly their music listening behavior shows conscious and unconscious diametric tendencies and influences. So, what could be more elegant than finding the underlying character given the attributes of a certain music piece and, as such, identifying the likelihood that music preference is also imprinted or at least resonating with its listener? This thesis focuses on the music audio attributes or the latent song features to determine human personality. Based on unsupervised learning, we cluster several large music datasets using multiple clustering techniques known to us. This analysis led us to classify song genres based on audio attributes, which can be deemed a novel contribution in the intersection of Music Information Retrieval (MIR) and human psychology studies. Existing research found a relationship between Myers-Briggs personality models and music genres. Our goal was to correlate audio attributes with the music genre, which will ultimately help us to determine user personality based on their music listening behavior from online music platforms. This target has been achieved as we showed the users’ spectral personality traits from the audio feature values of the songs they listen to online and verified our decision process with the help of a customized Music Recommendation System (MRS). Our model performs genre classification and personality detection with 78% and 74% accuracy, respectively. The results are promising compared to competitor approaches as they are explainable via statistics and visualizations. Furthermore, the RS completes and validates our pursuit through 81.3% accurate song suggestions. We believe the outcome of this thesis will work as an inspiration and assistance for fellow researchers in this arena to come up with more personalized song suggestions. As music preferences will shape specific user personality parameters, it is expected that more such elements will surface that would portray the daily activities of individuals and their underlying mentality