Xiaofang Wang

I am a Research Scientist at Meta Reality Lab. I received my Ph.D. from the Robotics Institute at Carnegie Mellon University (CMU), where I was fortunate to work with Prof. Kris Kitani. Before joining CMU, I received my B.S. in Computer Science from Peking University.

Google Scholar

Research

I am interested in computer vision, deep learning and machine learning. My recent focus is neural architecture search.

	Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection Xiaofang Wang, Kris M. Kitani International Conference on Robotics and Automation (ICRA), 2023 SOTA performance without SOTA detector architectures Simply scaling the backbone in SECOND can already match the state-of-the-art performance on the Waymo Open Dataset.
	Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M. Kitani, Yair Alon, Elad Eban International Conference on Learning Representations (ICLR), 2022 [Poster] [Google AI Blog] State-of-the-art efficiency without any architecture tuning Even the most simplistic method for building ensembles or cascades from existing pre-trained models can match or exceed the accuracy of state-of-the-art models while being drastically more efficient.
	Neighborhood-Aware Neural Architecture Search Xiaofang Wang, Shengcao Cao, Mengtian Li, Kris M. Kitani British Machine Vision Conference (BMVC), 2021 Finding flat-minima archtectures in the search space Towards better generalization, we propose a novel neighborhood-aware NAS formulation to identify flat-minima architectures in the search space.
	AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua European Conference on Computer Vision (ECCV), 2020 [Video-1 minute] [Video] [Slides] Automatically search for attention cells for video classification We propose a novel search space for spatiotemporal attention cells and a differentiable search method to learn attention cell designs.
	Learnable Embedding Space for Efficient Neural Architecture Compression Shengcao Cao, Xiaofang Wang*, Kris M. Kitani International Conference on Learning Representations (ICLR), 2019* * indicates equal contribution. [Code] [Poster] [Architecture Visualization] Automatically search for compressed architectures We propose to learn an embedding space for the architecture domain, based on which we present a compressed architecture search framework using Bayesian optimization.
	Error Correction Maximization for Deep Image Hashing Xiang Xu, Xiaofang Wang, Kris M. Kitani British Machine Vision Conference (BMVC), 2018
	Deep Supervised Hashing with Triplet Labels Xiaofang Wang, Yi Shi, Kris M. Kitani Asian Conference on Computer Vision (ACCV), 2016 Oral Presentation, (5.6% acceptance rate) [Code]
	Hamming Compatible Quantization for Hashing Zhe Wang, Ling-Yu Duan, Jie Lin, Xiaofang Wang, Tiejun Huang, Wen Gao International Joint Conference on Artificial Intelligence (IJCAI), 2015