most citedDETR++: Taming Your Multi-Scale Detection Transformer

7 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2022★ 3 cited

Towards Better Semantic Understanding of Mobile Interfaces

Srinivas Sunkara, Maria Wang, Lijuan Liu +6

Improving the accessibility and automation capabilities of mobile devices can have a significant positive impact on the daily lives of countless users. To stimulate research in thi…

cs.CV2022★ 7 cited

DETR++: Taming Your Multi-Scale Detection Transformer

Chi Zhang, Lijuan Liu, Xiaoxue Zang +4

Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12]. With the sweeping reform of Transformer…

cs.CV2021

Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering

Mingfei Chen, Jianfeng Zhang, Xiangyu Xu +4

In this work we develop a generalizable and efficient Neural Radiance Field (NeRF) pipeline for high-fidelity free-viewpoint human body synthesis under settings with sparse camera…

cs.IR2021

PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling

Xiaoxue Zang, Lijuan Liu, Maria Wang +3

We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in onlin emessaging. PhotoChat contains 12k dialogues, e…

cs.CV2021

In-game Residential Home Planning via Visual Context-aware Global Relation Learning

Lijuan Liu, Yin Yang, Yi Yuan +3

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home comp…

cs.CL2021★ 1 cited

ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces

Zecheng He, Srinivas Sunkara, Xiaoxue Zang +7

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibil…