Nitin Bansal
Texas A&M University
7 Papers
171 Citations
Nitin Bansal is an academic researcher from Texas A&M University. The author has contributed to research in topics: Computer science & Orthogonality. The author has an hindex of 3, co-authored 3 publications.
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Papers
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
Pengfei Zhu,Longyin Wen,Dawei Du,Xiao Bian,Haibin Ling,Qinghua Hu,Qinqin Nie,Hao Cheng,Chenfeng Liu,Xiaoyu Liu,Wenya Ma,Haotian Wu,Lianjie Wang,Arne Schumann,Chase Brown,Chen Qian,Chengzheng Li,Dongdong Li,Emmanouil Michail,Fan Zhang,Feng Ni,Feng Zhu,Guanghui Wang,Haipeng Zhang,Han Deng,Hao Liu,Haoran Wang,Heqian Qiu,Honggang Qi,Honghui Shi,Hongliang Li,Hongyu Xu,Hu Lin,Ioannis Kompatsiaris,Jian Cheng,Jianqiang Wang,Jianxiu Yang,Jingkai Zhou,Juanping Zhao,K J Joseph,Kaiwen Duan,Karthik Suresh,Bo Ke,Ke Wang,Konstantinos Avgerinakis,Lars Sommer,Lei Zhang,Li Yang,Lin Cheng,Lin Ma,Liyu Lu,Lu Ding,Minyu Huang,Naveen Kumar Vedurupaka,Nehal Mamgain,Nitin Bansal,Oliver Acatay,Panagiotis Giannakeris,Qian Wang,Qijie Zhao,Qingming Huang,Qiong Liu,Qishang Cheng,Qiuchen Sun,Robert Laganiere,Sheng Jiang,Shengjin Wang,Shubo Wei,Siwei Wang,Stefanos Vrochidis,Sujuan Wang,Tiaojio Lee,Usman Sajid,Vineeth N Balasubramanian,Wei Li,Wei Zhang,Weikun Wu,Wenchi Ma,Wenrui He,Wenzhe Yang,Xiaoyu Chen,Xin Sun,Xinbin Luo,Xintao Lian,Xiufang Li,Yangliu Kuai,Yali Li,Yi Luo,Yifan Zhang,Yiling Liu,Ying Li,Yong Wang,Yongtao Wang,Yuanwei Wu,Yue Fan,Yunchao Wei,Yuqin Zhang,Zexin Wang,Zhangyang Wang,Zhaoyue Xia,Zhen Cui,Zhenwei He,Zhipeng Deng,Zhiyao Guo,Zichen Song +104 more
- 08 Sep 2018
TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
•Proceedings Article
Can we gain more from orthogonality regularizations in training deep CNNs
Nitin Bansal,Xiaohan Chen,Zhangyang Wang +2 more
- 03 Dec 2018
TL;DR: This paper develops novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property to develop plug-and-play regularizations that can be conveniently incorporated into training almost any CNN without extra hassle.
•Posted Content
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs
TL;DR: In this article, orthogonality regularizations are proposed for training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property, which can be conveniently incorporated into training almost any CNN without extra hassle.
80
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
Jiacheng Liu,Pan Ji,Nitin Bansal,Changjiang Cai,Qingan Yan,Xiaolei Huang,Yi Xu +6 more
- 22 Mar 2022
TL;DR: The first work on 3D plane reconstruction within an end-to-end MVS framework that significantly outperforms state-of-the-art single-view plane reconstruction methods on both plane detection and 3D geometry metrics.
25
CLIP-FLow: Contrastive Learning by {s}emi-supervised Iterative Pseudo {l}abeling for Optical Flow Estimation
TL;DR: CLIP-Flow, a semi-supervised iterative pseudo labeling framework to transfer the pretraining knowledge to the target real domain, and proposes a contrastive loss on reference features and the warped features by pseudo ground truth flows, to further boost the accurate matching and dampen the mismatching.
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