Center for Research in Comptuer Vision
Center for Research in Comptuer Vision



Boqing Gong, PhD

Publications

(Google Scholar)

2017

Weighted Geodesic Flow Kernel for Interpersonal Mutual Influence Modeling and Emotion Recognition in Dyadic Interactions. Z. Yang, B. Gong, and S. Narayanan.
Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, October 2017. [PDF]

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes. Y. Zhang, P. David, and B. Gong.
Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, October 2017. [PDF] [extended version] [code]

VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation. C. Gan, Y. Li, H. Li, C. Sun, and B. Gong
Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, October 2017. [PDF] [Supp.] [dataset and code]

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach. A. Sharghi, J. Laurel, and B. Gong.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, June 2017. [PDF] [Supp.] [dataset and code]

Improving Facial Attribute Prediction using Semantic Segmentation. M. Kalayeh, B. Gong, and M. Shah.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, June 2017. [PDF].]

2016

Improved Dropout for Shallow and Deep Learning. Z. Li, B. Gong, and T. Yang.
Proceedings of the Neural Information Processing Systems (NIPS), Barcelona, Spain, Dec. 2016. [ArXiv]

Webly-Supervised Video Recognition by Mutually Voting for Relevant Web Images and Web Video Frames. C. Gan, C. Sun, L. Duan, and B. Gong.
Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, Oct. 2016. [PDF]

Query-Focused Extractive Video Summarization. A. Sharghi, B. Gong, and M. Shah.
Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, Oct. 2016. [PDF] [Supp.] [dataset and code]

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild. W-L. Chao*, S. Changpinyo*, B. Gong, and F. Sha.
Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, Oct. 2016. (Spotlight) [PDF] [Supp.]

Fast Zero-Shot Image Tagging. Y. Zhang, B. Gong, and M. Shah.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, June 2016. [PDF] [Supp.]

Learning Attributes Equals Multi-Source Domain Generalization. C. Gan, T. Yang, and B. Gong.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, June 2016. (Spotlight) [PDF]

Synthesized Classifiers for Zero-Shot Learning. S. Changpinyo, W. Chao, B. Gong, and F. Sha.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, June 2016. (Oral) [PDF] [Supp.]

2015 and before

Ph.D. Thesis: Kernel Methods for Unsupervised Domain Adaptation. [PDF]

Large-Margin Determinantal Point Processes. W. Chao*, B. Gong*, K. Grauman, and F. Sha.
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, Netherlands, July 2015. [PDF] [Supp.]

Diverse Sequential Subset Selection for Supervised Video Summarization. B. Gong*, W. Chao*, K. Grauman, and F. Sha.
Proceedings of the Neural Information Processing Systems (NIPS), Montreal, Canada, Dec. 2014. [PDF] [Supp.]

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition. B. Gong, K. Grauman, and F. Sha.
International Journal of Computer Vision (IJCV), Volume 109, Issue 1-2, pp 3-27, August 2014. [Project] [Link]

Reshaping Visual Datasets for Domain Adaptation. B. Gong, K. Grauman, and F. Sha.
Proceedings of the Neural Information Processing Systems (NIPS), Lake Tahoe, NV, Dec. 2013. [Project] [PDF] [Supp.] [Code]

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. B. Gong, K. Grauman, and F. Sha.
Proceedings of the International Conference on Machine Learning (ICML), Atlanta, GA, June 2013. (Oral) [Project] [PDF] [Supp.] [Slides] [Code]

Learning Semantic Signatures for 3D Object Retrieval. B. Gong, J. Liu, X. Wang, and X. Tang.
IEEE Transactions on Multimedia (TMM) Vol. 5, Issue 2, pp. 369-377, Feb. 2013. [PDF] [Demo-1] [Demo-2]

Geodesic Flow Kernel for Unsupervised Domain Adaptation. B. Gong, Y. Shi, F. Sha, and K. Grauman.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. (Oral) [Project] [PDF] [Supp.] [Slides] [Code&Data]

3D Object Retrieval with Semantic Attributes. B. Gong, J. Liu, X. Wang, and X. Tang.
Proceedings of the 19th ACM international conference on Multimedia(ACM MM), Scottsdale, Arizona, USA, 2011. [PDF] [Demo-1] [Demo-2]

Boosting 3D Object Retrieval by Object Flexibility. B. Gong, C. Xu, J. Liu, and X. Tang.
Proceedings of the 17th ACM international conference on Multimedia (ACM MM), Beijing, China, 2009. [PDF]

Automatic Facial Expression Recognition on a Single 3D Face by Exploring Shape Deformation. B. Gong, Y. Wang, J. Liu, and X. Tang.
Proceedings of the 17th ACM international conference on Multimedia (ACM MM), Beijing, China, 2009. [PDF]

Overcoming Dataset Bias: An Unsupervised Domain Adaptation Approach. B. Gong, F. Sha, and K. Grauman.
Big Data Meets Computer Vision: first international workshop on Large Scale Visual Recognition and Retrieval (BigVision) at NIPS, Lake Tahoe, NV, Dec. 2012. (Oral) [Project] [PDF] [Slides]

M.Phil. Thesis: 3D Object Retrieval and Recognition.