Search Results for author: Xiaofeng Gao

Found 19 papers, 6 papers with code

VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse

no code implementations23 Jun 2022 Yizhou Zhao, Steven Gong, Xiaofeng Gao, Wensi Ai, Song-Chun Zhu

With the recent progress of simulations by 3D modeling software and game engines, many researchers have focused on Embodied AI tasks in the virtual environment.

Indoor Scene Synthesis

Effects of Augmented-Reality-Based Assisting Interfaces on Drivers' Object-wise Situational Awareness in Highly Autonomous Vehicles

no code implementations6 Jun 2022 Xiaofeng Gao, Xingwei Wu, Samson Ho, Teruhisa Misu, Kumar Akash

To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator.

Autonomous Driving

HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction

no code implementations1 Jun 2022 Zuowu Zheng, Changwang Zhang, Xiaofeng Gao, Guihai Chen

Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph.

Click-Through Rate Prediction Recommendation Systems

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

no code implementations25 Apr 2022 Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen

To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution.

Collaborative Filtering

DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following

1 code implementation27 Feb 2022 Xiaofeng Gao, Qiaozi Gao, Ran Gong, Kaixiang Lin, Govind Thattai, Gaurav S. Sukhatme

Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.


Cross-Task Knowledge Distillation in Multi-Task Recommendation

no code implementations20 Feb 2022 Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, Guihai Chen

Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.

Knowledge Distillation Multi-Task Learning +1

Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

no code implementations6 Mar 2021 Xiaofeng Gao, Luyao Yuan, Tianmin Shu, Hongjing Lu, Song-Chun Zhu

Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground truth.

Motion Planning

Inductive Collaborative Filtering via Relation Graph Learning

no code implementations1 Jan 2021 Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Hongyuan Zha

In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.

Collaborative Filtering Graph Learning +1

Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

no code implementations24 Jul 2020 Xiaofeng Gao, Ran Gong, Yizhou Zhao, Shu Wang, Tianmin Shu, Song-Chun Zhu

Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.

Bayesian Inference Explanation Generation

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

1 code implementation9 Jul 2020 Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha

The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.

Collaborative Filtering Matrix Completion +2

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

no code implementations18 Feb 2020 Chaoqi Yang, Junwei Lu, Xiaofeng Gao, Haishan Liu, Qiong Chen, Gongshen Liu, Guihai Chen

Online real-time bidding (RTB) is known as a complex auction game where ad platforms seek to consider various influential key performance indicators (KPIs), like revenue and return on investment (ROI).


A Hierarchical Optimizer for Recommendation System Based on Shortest Path Algorithm

no code implementations7 Nov 2019 Jiacheng Dai, Zhifeng Jia, Xiaofeng Gao, Guihai Chen

Top-k Nearest Geosocial Keyword (T-kNGK) query on geosocial network is defined to give users k recommendations based on some keywords and designated spatial range, and can be realized by shortest path algorithms.

Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

1 code implementation NeurIPS 2019 Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen

We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations.

Imitation Learning

NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query

no code implementations26 Aug 2019 Xiuqi Huang, Yuanning Gao, Xiaofeng Gao, Guihai Chen

In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network.

Network Embedding

VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning

1 code implementation13 Mar 2019 Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, Song-Chun Zhu

One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.


Accelerate RNN-based Training with Importance Sampling

no code implementations31 Oct 2017 Fei Wang, Xiaofeng Gao, Guihai Chen, Jun Ye

Unfortunately, the calculation of the sampling probability distribution $P$ causes a major limitation of IS: it requires the input data to be well-structured, i. e., the feature vector is properly defined.

Stochastic Optimization

Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions

no code implementations1 Mar 2017 Tianmin Shu, Xiaofeng Gao, Michael S. Ryoo, Song-Chun Zhu

In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI).

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