tag.deep_learning.bib

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@article{SzSuLuTPAMI2020,
  author = {Szeto, R. and Sun, X. and Lu, K. and {\bf Corso}, {\bf J. J.}},
  grants = {FA8750-16-C-0168},
  journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  researcharea = {CV},
  tags = {computer vision, deep learning, video understanding, video inpainting, video prediction, frame interpolation},
  title = {{A Temporally-Aware Interpolation Network for Video Frame Inpainting}},
  venue = {TPAMI},
  year = {2020 (to appear)}
}
@inproceedings{ZhPaZhAAAI2020,
  author = {Zhou, L. and Palangi, H. and Zhang, L. and Hu, H. and {\bf Corso}, {\bf J. J.} and Gao, J.},
  booktitle = {Proceedings of AAAI Conference on Artificial Intelligence},
  grants = {NIST 60NANB17D191, DARPA FA8750-17-2-0112},
  researcharea = {CV},
  tags = {computer vision, pretraining, deep learning, image captioning, vision and language, VQA},
  title = {Unified Vision-Language Pre-Training for Image Captioning and VQA},
  venue = {AAAI},
  year = {2020}
}
@inproceedings{MiCoICCV2019,
  author = {Min, K. and {\bf Corso}, {\bf J. J.}},
  booktitle = {{Proceedings of IEEE International Conference on Computer Vision}},
  grants = {NIST 60NANB17D191},
  researcharea = {CV},
  tags = {computer vision, video understanding, video saliency, deep learning},
  title = {{TASED}-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection},
  url = {http://openaccess.thecvf.com/content_ICCV_2019/papers/Min_TASED-Net_Temporally-Aggregating_Spatial_Encoder-Decoder_Network_for_Video_Saliency_Detection_ICCV_2019_paper.pdf},
  venue = {ICCV},
  year = {2019}
}
@inproceedings{HuZhZhBMVC2019,
  author = {Huang, H. and Zhou, L. and Zhang, W. and {\bf Corso}, {\bf J. J.} and Xu, C.},
  booktitle = {Proceedings of the British Machine Vision Conference},
  grants = {DARPA FA8750-17-2-0125, NSF IIS 1522904, NIST 60NANB17D191},
  researcharea = {CV},
  tags = {computer vision, video understanding, deep learning, activity recognition, object-object interaction},
  title = {Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition},
  url = {https://bmvc2019.org/wp-content/uploads/papers/0524-paper.pdf},
  venue = {BMVC},
  year = {2019}
}
@inproceedings{TaXuYaCVPR2019,
  author = {Tang, H. and Xu, D. and Yan, Y. and Wang, Y. and {\bf Corso}, {\bf J. J.} and Sebe, N.},
  booktitle = {{Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}},
  grants = {NIST 60NANB17D191},
  projects = {selected},
  researcharea = {CV},
  tags = {computer vision, deep learning},
  title = {Multi-Channel Attention Selection {GAN} with Cascaded Semantic Guidance for Cross-View Image Translation},
  url = {http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Multi-Channel_Attention_Selection_GAN_With_Cascaded_Semantic_Guidance_for_Cross-View_CVPR_2019_paper.pdf},
  venue = {CVPR},
  year = {2019}
}
@techreport{ElCoARXIV2018,
  author = {{El Banani}, M. and {\bf Corso}, {\bf J. J.}},
  codedownload = {https://github.com/mbanani/adviser_networks},
  institution = {ARXIV},
  number = {1802.01666},
  researcharea = {CV},
  tags = {computer vision, human-in-the-loop, deep learning, viewpoint estimation},
  title = {Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation},
  url = {https://arxiv.org/abs/1802.01666},
  venue = {ARXIV},
  year = {2018}
}
@techreport{HoGaCoARXIV2018,
  author = {Hofesmann, E. and Ganesh, M. R. and {\bf Corso}, {\bf J. J.}},
  codedownload = {https://github.com/MichiganCOG/M-PACT},
  institution = {ARXIV},
  number = {1804.05879},
  researcharea = {CV},
  tags = {computer vision, video understanding, deep learning, activity recognition},
  title = {{M-PACT}: An Open Source Platform for Repeatable Activity Classification Research},
  url = {https://arxiv.org/abs/1804.05879},
  venue = {ARXIV},
  year = {2018}
}
@techreport{GaHoMiARXIV2018,
  author = {Ganesh, M. R. and Hofesmann, E. and Min, B. and Gafoor, N. and {\bf Corso}, {\bf J. J.}},
  institution = {ARXIV},
  number = {1803.08094},
  researcharea = {CV},
  tags = {computer vision, video understanding, deep learning, activity recognition},
  title = {T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for Action Recognition},
  url = {https://arxiv.org/abs/1803.08094},
  venue = {ARXIV},
  year = {2018}
}
@techreport{PaGrKuARXIV2018,
  author = {Patel, S. and Griffin, B. and Kusano, K. and {\bf Corso}, {\bf J. J.}},
  institution = {ARXIV},
  number = {1801.04340},
  researcharea = {ML},
  tags = {deep learning, action prediction, autonomous driving},
  title = {Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models},
  url = {https://arxiv.org/abs/1801.04340},
  venue = {ARXIV},
  year = {2018}
}
@inproceedings{ZhLoCoBMVC2018,
  author = {Zhou, L. and Louis, N. and {\bf Corso}, {\bf J. J.}},
  booktitle = {Proceedings of British Machine Vision Conference},
  grants = {aro2015, d3m},
  projects = {selected, aro2015, d3m},
  researcharea = {CV},
  tags = {video understanding, video to text, computer vision, object grounding, deep learning},
  title = {Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction},
  url = {http://bmvc2018.org/contents/papers/0070.pdf},
  venue = {BMVC},
  year = {2018}
}
@inproceedings{SuSzCoACCV2018,
  author = {Sun, X. and Szeto, R. and {\bf Corso}, {\bf J. J.}},
  booktitle = {Proceedings of Asian Conference on Computer Vision (ACCV)},
  codedownload = {https://github.com/sunxm2357/TAI_video_frame_inpainting},
  projectpage = {http://ryanszeto.com/projects/tai.html},
  projects = {medifor},
  researcharea = {CV},
  tags = {video understanding, deep learning, computer vision, video prediction, inpainting},
  title = {{A Temporally-Aware Interpolation Network for Video Frame Inpainting}},
  url = {https://arxiv.org/abs/1803.07218},
  venue = {ACCV},
  year = {2018}
}
@article{SaCoGuTMI2017,
  author = {Sarikaya, D. and {\bf Corso}, {\bf J. J.} and Guru, K. A.},
  journal = {{IEEE Transactions on Medical Imaging}},
  number = {7},
  pages = {1542--1549},
  projects = {surgrobots, video, medimage},
  researcharea = {MI},
  tags = {deep learning, medical imaging, surgical robotics},
  title = {Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection},
  venue = {TMI},
  volume = {36},
  year = {2017}
}
@inproceedings{XuXiChAAAI2015,
  author = {Xu, R. and Xiong, C. and Chen, W. and {\bf Corso}, {\bf J. J.}},
  booktitle = {Proceedings of AAAI Conference on Artificial Intelligence},
  grants = {aroyip,career,mindseye},
  projects = {selected,career,mindseye,video,videototext},
  researcharea = {CV},
  tags = {computer vision, natural language, video summarization, video understanding, video to text, deep learning},
  title = {Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework},
  url = {http://web.eecs.umich.edu/~jjcorso/pubs/xu_corso_AAAI2015_v2t.pdf},
  venue = {AAAI},
  year = {2015}
}