no code implementations • 15 Apr 2023 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang, Rama Chellappa
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.
2 code implementations • 21 Apr 2022 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Archana Venkatachalapathy, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Alice Li, Shangru Li, Rama Chellappa
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
1 code implementation • CVPR 2022 • Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko
Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.
1 code implementation • 25 Apr 2021 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Yue Yao, Liang Zheng, Pranamesh Chakraborty, Christian E. Lopez, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff
Track 3 addressed city-scale multi-target multi-camera vehicle tracking.
1 code implementation • 12 Jan 2021 • Qi Feng, Vitaly Ablavsky, Stan Sclaroff
In this paper, we focus on two foundational tasks: the Vehicle Retrieval by NL task and the Vehicle Tracking by NL task, which take advantage of the proposed CityFlow-NL benchmark and provide a strong basis for future research on the multi-target multi-camera tracking by NL description task.
no code implementations • 12 Feb 2020 • Nataniel Ruiz, Hao Yu, Danielle A. Allessio, Mona Jalal, Ajjen Joshi, Thomas Murray, John J. Magee, Jacob R. Whitehill, Vitaly Ablavsky, Ivon Arroyo, Beverly P. Woolf, Stan Sclaroff, Margrit Betke
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
1 code implementation • 23 Dec 2019 • Nuno C. Garcia, Sarah Adel Bargal, Vitaly Ablavsky, Pietro Morerio, Vittorio Murino, Stan Sclaroff
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
1 code implementation • CVPR 2021 • Qi Feng, Vitaly Ablavsky, Qinxun Bai, Stan Sclaroff
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task.
1 code implementation • ECCV 2020 • Chenhongyi Yang, Vitaly Ablavsky, Kaihong Wang, Qi Feng, Margrit Betke
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly.
no code implementations • 26 Jul 2019 • Qi Feng, Vitaly Ablavsky, Qinxun Bai, Guorong Li, Stan Sclaroff
In benchmarks, our method is competitive with state of the art trackers, while it outperforms all other trackers on targets with unambiguous and precise language annotations.
no code implementations • ICCV 2019 • Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky
We consider the problem of fine-grained classification on an edge camera device that has limited power.