Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Classical close-set semantic segmentation networks have limited ability to detect out-of-
distribution (OOD) objects, which is important for safety-critical applications such as …

In Audio-Video Emotion Recognition (AVER), the idea is to have a human-level
understanding of emotions from video clips. There is a need to bring these two modalities …

With the increasing popularity of micro-video sharing where people shoot short-videos
effortlessly and share their daily stories on social media platforms, the micro-video …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …

Conventional deep-learning-based retrieval models are generally trained under the
framework of scene classification with cross-entropy loss, this way focuses only on the …