34 citations · 37 across the 7 of their papers we have counts for
8 papers · 1 filter
Training with Product Digital Twins for AutoRetail Checkout
Yue Yao, Xinyu Tian, Zheng Tang +4
Automating the checkout process is important in smart retail, where users effortlessly pass products by hand through a camera, triggering automatic product detection, tracking, and…
The 7th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +17
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems…
Large-scale Training Data Search for Object Re-identification
Yue Yao, Huan Lei, Tom Gedeon +1
We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training…
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond
Yue Yao, Liang Zheng, Xiaodong Yang +2
This article aims to use graphic engines to simulate a large number of training data that have free annotations and possibly strongly resemble to real-world data. Between synthetic…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
Pairwise-GAN: Pose-based View Synthesis through Pair-Wise Training
Xuyang Shen, Jo Plested, Yue Yao +1
Three-dimensional face reconstruction is one of the popular applications in computer vision. However, even state-of-the-art models still require frontal face as inputs, which restr…
Simulating Content Consistent Vehicle Datasets with Attribute Descent
Yue Yao, Liang Zheng, Xiaodong Yang +2
This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content…