Time-based Sequence Model for Personalization and Recommendation Systems

1 code implementation • 27 Aug 2020 • Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong

In this paper we develop a novel recommendation model that explicitly incorporates time information.

Recommendation Systems

Deep Learning Recommendation Model for Personalization and Recommendation Systems

18 code implementations • 31 May 2019 • Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.

Deep Learning Recommendation Systems

Joint Multi-Person Pose Estimation and Semantic Part Segmentation

no code implementations • CVPR 2017 • Fangting Xia, Peng Wang, Xianjie Chen, Alan Yuille

To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments.

Human Detection Multi-Person Pose Estimation

DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization

no code implementations • 23 Nov 2015 • Jun Zhu, Xianjie Chen, Alan L. Yuille

In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.

Object object-detection +1

Ground-truth dataset and baseline evaluations for image base-detail separation algorithms

no code implementations • 21 Nov 2015 • Xuan Dong, Boyan Bonev, Weixin Li, Weichao Qiu, Xianjie Chen, Alan Yuille

Base-detail separation is a fundamental computer vision problem consisting of modeling a smooth base layer with the coarse structures, and a detail layer containing the texture-like structures.

Parsing Occluded People by Flexible Compositions

no code implementations • CVPR 2015 • Xianjie Chen, Alan Yuille

We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model.

Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

no code implementations • NeurIPS 2014 • Xianjie Chen, Alan Yuille

More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations).

Pose Estimation

Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts

no code implementations • CVPR 2014 • Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, Alan Yuille

Our model automatically decouples the holistic object or body parts from the model when they are hard to detect.

Object Semantic Part Detection

The Role of Context for Object Detection and Semantic Segmentation in the Wild

no code implementations • CVPR 2014 • Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, Alan Yuille

In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches.

object-detection Object Detection +2