2 research outputs found
PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability
Fashion is an important part of human experience. Events such as interviews,
meetings, marriages, etc. are often based on clothing styles. The rise in the
fashion industry and its effect on social influencing have made outfit
compatibility a need. Thus, it necessitates an outfit compatibility model to
aid people in clothing recommendation. However, due to the highly subjective
nature of compatibility, it is necessary to account for personalization. Our
paper devises an attribute-wise interpretable compatibility scheme with
personal preference modelling which captures user-item interaction along with
general item-item interaction. Our work solves the problem of interpretability
in clothing matching by locating the discordant and harmonious attributes
between fashion items. Extensive experiment results on IQON3000, a publicly
available real-world dataset, verify the effectiveness of the proposed model.Comment: 10 pages, 5 figures, to be published in IEEE BigMM, 202
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Unsupervised crowd counting is a challenging yet not largely explored task.
In this paper, we explore it in a transfer learning setting where we learn to
detect and count persons in an unlabeled target set by transferring
bi-knowledge learnt from regression- and detection-based models in a labeled
source set. The dual source knowledge of the two models is heterogeneous and
complementary as they capture different modalities of the crowd distribution.
We formulate the mutual transformations between the outputs of regression- and
detection-based models as two scene-agnostic transformers which enable
knowledge distillation between the two models. Given the regression- and
detection-based models and their mutual transformers learnt in the source, we
introduce an iterative self-supervised learning scheme with
regression-detection bi-knowledge transfer in the target. Extensive experiments
on standard crowd counting benchmarks, ShanghaiTech, UCF\_CC\_50, and UCF\_QNRF
demonstrate a substantial improvement of our method over other
state-of-the-arts in the transfer learning setting.Comment: This paper has been accepted by ACM MM 2020(Oral