Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields.
Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.
Adapting semantic segmentation models to new domains is an important but challenging problem.
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database.
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia.
The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments.
However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL).
Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.