73 citations · 184 across the 16 of their papers we have counts for
16 papers
Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection
Xiaofang Wang, Kris M. Kitani
Considerable research effort has been devoted to LiDAR-based 3D object detection and empirical performance has been significantly improved. While progress has been encouraging, we…
Neighborhood-Aware Neural Architecture Search
Xiaofang Wang, Shengcao Cao, Mengtian Li +1
Existing neural architecture search (NAS) methods often return an architecture with good search performance but generalizes poorly to the test setting. To achieve better generaliza…
Efficient Model Performance Estimation via Feature Histories
Shengcao Cao, Xiaofang Wang, Kris Kitani
An important step in the task of neural network design, such as hyper-parameter optimization (HPO) or neural architecture search (NAS), is the evaluation of a candidate model's per…
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Xiaofang Wang, Dan Kondratyuk, Eric Christiansen +3
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones. While ensembles and cascades are well-known techniques that were proposed be…
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann +5
Convolutional operations have two limitations: (1) do not explicitly model where to focus as the same filter is applied to all the positions, and (2) are unsuitable for modeling lo…
Point in, Box out: Beyond Counting Persons in Crowds
Yuting Liu, Miaojing Shi, Qijun Zhao +1
Modern crowd counting methods usually employ deep neural networks (DNN) to estimate crowd counts via density regression. Despite their significant improvements, the regression-base…
Learnable Embedding Space for Efficient Neural Architecture Compression
Shengcao Cao, Xiaofang Wang, Kris M. Kitani
We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compr…
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning
Maxime Petit, Amaury Depierre, Xiaofang Wang +2
We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimiz…
Error Correction Maximization for Deep Image Hashing
Xiang Xu, Xiaofang Wang, Kris M. Kitani
We propose to use the concept of the Hamming bound to derive the optimal criteria for learning hash codes with a deep network. In particular, when the number of binary hash codes (…
Image Registration Based Flicker Solving in Video Face Replacement and Analysis Based Sub-pixel Image Registration
Xiaofang Wang, Guoqiang Xiang, Xinyue Zhang +1
In this paper, a framework of video face replacement is proposed and it deals with the flicker of swapped face in video sequence. This framework contains two main innovations: 1) t…
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
Yuxing Tang, Josiah Wang, Xiaofang Wang +4
Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large numbe…
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Lingkun Luo, Liming Chen, Shiqiang Hu +2
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation o…
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Lingkun Luo, Xiaofang Wang, Shiqiang Hu +1
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unl…
Close Yet Distinctive Domain Adaptation
Lingkun Luo, Xiaofang Wang, Shiqiang Hu +3
Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this p…
Deep Supervised Hashing with Triplet Labels
Xiaofang Wang, Yi Shi, Kris M. Kitani
Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent ima…
Contextual Visual Similarity
Xiaofang Wang, Kris M. Kitani, Martial Hebert
Measuring visual similarity is critical for image understanding. But what makes two images similar? Most existing work on visual similarity assumes that images are similar because…