@article {2022,
	title = {EmoMV: Affective Music-Video Correspondence Learning Datasets for Classification and Retrieval},
	journal = {Information Fusion},
	year = {2022},
	abstract = {Studies in affective audio-visual correspondence learning require ground-truth data to train, validate, and test models. The number of available datasets together with benchmarks, however, is still limited. In this paper, we create a collection of three datasets (called EmoMV) for affective correspondence learning between music and video modalities. The first two datasets (called EmoMVA, and EmoMV-B, respectively) are constructed by making use of music video segments from other available datasets. The third one called EmoMV-C is created from music videos that we self-collected from YouTube. The music-video pairs in our datasets are annotated as matched or mismatched in terms of the emotions they are conveying. The emotions are annotated by humans in the EmoMV-A dataset, while in the EmoMV-B and EmoMV-C datasets they are predicted using a pretrained deep neural network. A user study is carried out to evaluate the accuracy of the {\textquotedblleft}matched{\textquotedblright} and {\textquotedblleft}mismatched{\textquotedblright} labels offered in the EmoMV dataset collection. In addition to creating three new datasets, a benchmark deep neural network model for binary affective music-video correspondence classification is also proposed. This proposed benchmark model is then modified to adapt to affective music-video retrieval. Extensive experiments are carried out on all three datasets of the EmoMV collection. Experimental results demonstrate that our proposed model outperforms state-of-the-art approaches on both the binary classification and retrieval tasks. We envision that our newly created dataset collection together with the proposed benchmark models will facilitate advances in affective computing research.

Dataset available at: https://zenodo.org/record/7011072$\#$.YzpFoqTmgzZ},
	doi = {https://doi.org/10.1016/j.inffus.2022.10.002},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S1566253522001725},
	author = {Quang-Hieu Pham and D. Herremans and G. Roig}
}
@mastersthesis {2020,
	title = {Data-driven 3D Scene Understanding},
	volume = {PhD},
	year = {2020},
	school = {SUTD},
	address = {Singapore},
	abstract = {Among all digital representations we have for real physical objects, 3D is arguably the most expressive encoding. 3D representations allow storage and manipulation of high-level information as well as low-level features. However, it is still not clear how humans perceive 3D data innately. Replicating these capabilities in a vision-based agent is a challenging problem that can make an impact in several applications such as autonomous driving, robotics, and augmented/virtual reality. Scene understanding, which can be understood as the act of analyzing a scene by considering its geometric and semantic properties, is a long standing problem in computer vision. Recently, with the rise of big data and deep learning, this problem has gained a lot of attention over the past few years. The main issue lies in how to collect and annotate 3D data effectively, while making use of these data to learn a good representation of the world.

In this thesis, a series of topics on data-driven 3D scene understanding is discussed. Under the guiding principle of leveraging large-scale data for scene understanding, my efforts have led to several state-of-the-art algorithms, and the creation of multiple large-scale datasets. First, we describe the process of collecting and annotating an indoor scene dataset. Next, we discuss a method to learn local cross-domain features, which can be applied to several low-level scene understanding tasks. We then take a look at the problem of progressive semantic segmentation and joint semantic-instance segmentation for indoor scenes. Finally, we dabble into the field of outdoor scene understanding by showing the creation of an autonomous driving dataset.},
	author = {Quang-Hieu Pham}
}
