Xiaolong Wang

Select All Publication by Year Self-Supervised Learning Video Understanding Common Sense Reasoning RL and Robotics 3D Interaction Dexterous Hand

Jiarui Xu, Yossi Gandelsman, Amir Bar, Jianwei Yang, Jianfeng Gao, Trevor Darrell, Xiaolong Wang.
IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks.
arXiv, 2023.

[arXiv] [project page]

Renhao Wang, Yu Sun, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang.
Test-Time Training on Video Streams.
arXiv, 2023.

[arXiv] [project page] [code and dataset]

Yang Fu, Ishan Misra, Xiaolong Wang.
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses.
International Conference on Machine Learning (ICML), 2023.

[arXiv] [project page]

Jiteng Mu, Shalini De Mello, Zhiding Yu, Nuno Vasconcelos, Xiaolong Wang, Jan Kautz, Sifei Liu.
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs.
Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[arXiv] [project page] [video]

Xuanchi Ren, Xiaolong Wang.
Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image.
Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[arXiv] [project page] [video] [code]

Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
GroupViT: Semantic Segmentation Emerges from Text Supervision.
Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[arXiv] [project page] [video] [code] [huggingface colab] [huggingface demo]

Yizhuo Li*, Miao Hao*, Zonglin Di*, Nitesh B. Gundavarapu, Xiaolong Wang.
Test-Time Personalization with a Transformer for Human Pose Estimation.
Conference on Neural Information Processing Systems (NeurIPS), 2021.

[arXiv] [project page] [code]

Zihang Lai, Sifei Liu, Alexei A. Efros, Xiaolong Wang.
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion.
International Conference on Computer Vision (ICCV), 2021 (Oral Presentation).

[arXiv] [project page] [code] [video]

Jiarui Xu, Xiaolong Wang.
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective.
International Conference on Computer Vision (ICCV), 2021 (Oral Presentation).

[arXiv] [project page] [code]

Haiping Wu, Xiaolong Wang.
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency.
International Conference on Computer Vision (ICCV), 2021.

[arXiv] [project page] [code]

Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, Trevor Darrell.
Robust Object Detection via Instance-Level Temporal Cycle Confusion.
International Conference on Computer Vision (ICCV), 2021.

[arXiv] [project page] [code]

Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, Trevor Darrell.
Region Similarity Representation Learning.
International Conference on Computer Vision (ICCV), 2021.

[arXiv] [code]

Yinbo Chen, Sifei Liu, Xiaolong Wang.
Learning Continuous Image Representation with Local Implicit Image Function.
Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Oral Presentation).

[arXiv] [code] [project page]

Nicklas Hansen, Xiaolong Wang.
Generalization in Reinforcement Learning by Soft Data Augmentation.
International Conference on Robotics and Automation (ICRA), 2021.

[arXiv] [code] [project page]

Qiang Zhang, Tete Xiao, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang.
Learning Cross-domain Correspondence for Control with Dynamics Cycle-consistency.
International Conference on Learning Representations (ICLR), 2021 (Oral Presentation).

[arXiv] [code] [project page]

Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang.
Self-Supervised Policy Adaptation during Deployment.
International Conference on Learning Representations (ICLR), 2021 (Spotlight Presentation).

[arXiv] [code] [project page] [bair blog post]

Tete Xiao, Xiaolong Wang, Alexei A. Efros, Trevor Darrell.
What Should Not Be Contrastive in Contrastive Learning.
International Conference on Learning Representations (ICLR), 2021.

[arXiv]

Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz.
Online Adaptation for Consistent Mesh Reconstruction in the Wild.
Conference on Neural Information Processing Systems (NeurIPS), 2020.

[pdf] [project page]

Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt.
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts.
International Conference on Machine Learning (ICML), 2020.

[arXiv] [code and project page] [BibTeX]

Xueting Li*, Sifei Liu*, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang.
Joint-task Self-supervised Learning for Temporal Correspondence.
Conference on Neural Information Processing Systems (NeurIPS), 2019.

[arXiv] [project page] [BibTeX] [code]

Xiaolong Wang, Allan Jabri* and Alexei A. Efros.
Learning Correspondence from the Cycle-consistency of Time.
Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral Presentation).
(*indicates equal contributions.)

[project page] [slides] [result video] [oral talk]
[arXiv] [BibTeX] [code]

Xiaolong Wang, Kaiming He, and Abhinav Gupta.
Transitive Invariance for Self-supervised Visual Representation Learning.
International Conference on Computer Vision (ICCV), 2017

[pdf] [BibTeX] [caffe_model(RGB order input)] [caffe_prototxt]

Xiaolong Wang and Abhinav Gupta.
Generative Image Modeling using Style and Structure Adversarial Networks.
European Conference on Computer Vision (ECCV), 2016

[pdf] [BibTeX] [code] [models and dataset]

Xiaolong Wang and Abhinav Gupta.
Unsupervised Learning of Visual Representations using Videos.
International Conference on Computer Vision (ICCV), 2015

[pdf] [BibTeX] [code] [model] [mined_patches] [project page] [spotlight video]

Xiaolong Wang, David F. Fouhey, and Abhinav Gupta.
Designing Deep Networks for Surface Normal Estimation.
Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[pdf] [BibTeX] [results for NYU Depth V2] [code and models] [project page]