1 code implementation • 5 Nov 2023 • Jingru Yi, Burak Uzkent, Oana Ignat, Zili Li, Amanmeet Garg, Xiang Yu, Linda Liu
While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks.
no code implementations • 13 Jan 2023 • Miao Yin, Burak Uzkent, Yilin Shen, Hongxia Jin, Bo Yuan
We first develop a graph-based ranking for measuring the importance of attention heads, and the extracted importance information is further integrated to an optimization-based procedure to impose the heterogeneous structured sparsity patterns on the ViT models.
no code implementations • CVPR 2023 • Burak Uzkent, Amanmeet Garg, Wentao Zhu, Keval Doshi, Jingru Yi, Xiaolong Wang, Mohamed Omar
For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks.
no code implementations • CVPR 2022 • Qian Lou, Yen-Chang Hsu, Burak Uzkent, Ting Hua, Yilin Shen, Hongxia Jin
The key primitive is that Dictionary-Lookup-Transformormations (DLT) is proposed to replace Linear Transformation (LT) in multi-modal detectors where each weight in Linear Transformation (LT) is approximately factorized into a smaller dictionary, index, and coefficient.
2 code implementations • ICLR 2021 • Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #6 on Image Generation on CIFAR-100
no code implementations • 20 Nov 2020 • Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon
Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
1 code implementation • ICCV 2021 • Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Ranked #5 on Semantic Segmentation on SpaceNet 1 (using extra training data)
1 code implementation • 15 Jun 2020 • Jihyeon Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David Lobell, Stefano Ermon
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.
no code implementations • 7 Jun 2020 • Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.
no code implementations • 11 Apr 2020 • Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data.
2 code implementations • CVPR 2020 • Burak Uzkent, Stefano Ermon
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e. g. remote sensing, they can be much more expensive to acquire.
no code implementations • 5 Feb 2020 • Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
3 code implementations • 14 Dec 2019 • Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
Ranked #6 on Cloud Removal on SEN12MS-CR-TS
3 code implementations • 9 Dec 2019 • Burak Uzkent, Christopher Yeh, Stefano Ermon
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images.
3 code implementations • 7 May 2019 • Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data.
no code implementations • 5 May 2019 • Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David Lobell, Marshall Burke, Stefano Ermon
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.
no code implementations • 19 Sep 2018 • Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data.
2 code implementations • 20 Jan 2018 • Burak Uzkent, Young-Woo Seo
Experimental results showed that the performance of ours is, on average, 70. 10% for precision at 20 pixels, 53. 00% for success rate for the OTB100 data, and 54. 50% and 40. 2% for the UAV123 data.
no code implementations • 20 Nov 2017 • Burak Uzkent, Aneesh Rangnekar, Matthew J. Hoffman
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions.
no code implementations • 12 Jul 2017 • Burak Uzkent, Aneesh Rangnekar, M. J. Hoffman
Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks.