To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model.
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
Previous studies show effective of pre-trained language models for sentiment analysis.
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers.
Ranked #1 on Lipreading on CMLR
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.