no code implementations • 5 Nov 2023 • Bruce Levin, Erik Learned-Miller
We show that the likelihood function for a multinomial vector observed under arbitrary interval censoring constraints on the frequencies or their partial sums is completely log-concave by proving that the constrained sample spaces comprise M-convex subsets of the discrete simplex.
no code implementations • 7 Oct 2023 • Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau
Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment.
no code implementations • ICCV 2023 • Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, Erik Learned-Miller
We present an approach to estimating camera rotation in crowded, real-world scenes from handheld monocular video.
no code implementations • 29 Jun 2023 • Zitian Chen, Mingyu Ding, Yikang Shen, Wei Zhan, Masayoshi Tomizuka, Erik Learned-Miller, Chuang Gan
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently.
no code implementations • 15 May 2023 • Shifan Zhu, Zhipeng Tang, Michael Yang, Erik Learned-Miller, Donghyun Kim
Our paper proposes a direct sparse visual odometry method that combines event and RGB-D data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors.
no code implementations • 18 Jan 2023 • Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
no code implementations • 15 Dec 2022 • Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik Learned-Miller, Chuang Gan
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
no code implementations • CVPR 2023 • Ashish Singh, Michael J. Jones, Erik Learned-Miller
We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes.
Ranked #16 on Anomaly Detection on ShanghaiTech
no code implementations • 13 Nov 2022 • Ashutosh Singh, Ashish Singh, Aria Masoomi, Tales Imbiriba, Erik Learned-Miller, Deniz Erdogmus
Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well.
1 code implementation • 21 Apr 2022 • Tejas Panambur, Deep Chakraborty, Melissa Meyer, Ralph Milliken, Erik Learned-Miller, Mario Parente
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability.
no code implementations • 28 Feb 2022 • Pia Bideau, Erik Learned-Miller, Cordelia Schmid, Karteek Alahari
In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly.
1 code implementation • 27 Dec 2021 • Gopal Sharma, Bidya Dash, Aruni RoyChowdhury, Matheus Gadelha, Marios Loizou, Liangliang Cao, Rui Wang, Erik Learned-Miller, Subhransu Maji, Evangelos Kalogerakis
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
1 code implementation • ICCV 2021 • Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau
In particular, we model the aligned data as a spatio-temporal Poisson point process.
1 code implementation • 9 May 2021 • Chen Qu, Hamed Zamani, Liu Yang, W. Bruce Croft, Erik Learned-Miller
We first conduct sparse retrieval with BM25 and study expanding the question with object names and image captions.
1 code implementation • NeurIPS 2021 • Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas
When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy.
1 code implementation • 31 Mar 2021 • Huaizu Jiang, Erik Learned-Miller
When sampling correspondences to build the cost volume, a large neighborhood radius is required to deal with large displacements, introducing a significant computational burden.
no code implementations • 16 Dec 2020 • Lijun Zhang, Xiao Liu, Erik Learned-Miller, Hui Guan
When capturing images in low-light conditions, the images often suffer from low visibility, which not only degrades the visual aesthetics of images, but also significantly degenerates the performance of many computer vision algorithms.
no code implementations • 6 Oct 2020 • Zitian Chen, Subhransu Maji, Erik Learned-Miller
To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting.
no code implementations • ECCV 2020 • Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn, Erik Learned-Miller, Manmohan Chandraker
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
no code implementations • 26 Jun 2020 • Zitian Chen, Zhiqiang Shen, Jiahui Yu, Erik Learned-Miller
After learning a new object category from image-level annotations (with no object bounding boxes), humans are remarkably good at precisely localizing those objects.
1 code implementation • ECCV 2020 • Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, Subhransu Maji
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
2 code implementations • CVPR 2020 • Huaizu Jiang, Ishan Misra, Marcus Rohrbach, Erik Learned-Miller, Xinlei Chen
Popularized as 'bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA).
Ranked #18 on Visual Question Answering (VQA) on VQA v2 test-std
1 code implementation • ICCV 2019 • Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, Jan Kautz
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.
no code implementations • 15 May 2019 • Erik Learned-Miller, Philip S. Thomas
We present a new method for constructing a confidence interval for the mean of a bounded random variable from samples of the random variable.
1 code implementation • CVPR 2019 • Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, Erik Learned-Miller
Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.
2 code implementations • CVPR 2019 • Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, Jan Kautz
In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.
no code implementations • 14 Feb 2019 • Colin Samplawski, Heesung Kwon, Erik Learned-Miller, Benjamin M. Marlin
Zero-shot learning (ZSL) is one of the most extreme forms of learning from scarce labeled data.
no code implementations • 2 Feb 2019 • Marwan Mattar, Michael Ross, Erik Learned-Miller
Congealing is a flexible nonparametric data-driven framework for the joint alignment of data.
1 code implementation • 27th ACM International Conference on Information and Knowledge Management (CIKM '18) 2018 • Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, Erik Learned-Miller, and Jaap Kamps
In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document.
Ranked #12 on Ad-Hoc Information Retrieval on TREC Robust04
no code implementations • ECCV 2018 • SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, Erik Learned-Miller
In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.
no code implementations • CVPR 2018 • Pia Bideau, Aruni RoyChowdhury, Rakesh R. Menon, Erik Learned-Miller
Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding.
no code implementations • ECCV 2018 • Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich
As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth.
5 code implementations • CVPR 2018 • Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
Finally, the two input images are warped and linearly fused to form each intermediate frame.
no code implementations • ICCV 2017 • SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
no code implementations • 7 Sep 2017 • SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
1 code implementation • NeurIPS 2017 • Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
Self-paced learning and hard example mining re-weight training instances to improve learning accuracy.
no code implementations • 31 Oct 2016 • Pia Bideau, Erik Learned-Miller
The second is to report on new versions of three previously existing data sets that are compatible with this definition.
no code implementations • 13 Sep 2016 • Li Yang Ku, Erik Learned-Miller, Rod Grupen
We demonstrate that this approach outperforms baseline approaches in cluttered scenarios on the grasping dataset and a point cloud based approach on a grasping task using Robonaut-2.
1 code implementation • 10 Jun 2016 • Huaizu Jiang, Erik Learned-Miller
The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks.
no code implementations • 1 Apr 2016 • Pia Bideau, Erik Learned-Miller
The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer, and even camouflage.
no code implementations • 5 Nov 2015 • Manjunath Narayana, Allen Hanson, Erik Learned-Miller
In particular, it is essential to have a background likelihood, a foreground likelihood, and a prior at each pixel.
no code implementations • 5 Nov 2015 • Manjunath Narayana, Allen Hanson, Erik Learned-Miller
In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussians at each pixel [7], to kernel density estimates at each pixel [1], and more recently to joint domainrange density estimates that incorporate spatial information [6].
no code implementations • 5 Nov 2015 • Manjunath Narayana, Allen Hanson, Erik Learned-Miller
Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene.
no code implementations • 3 Jun 2015 • Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, Erik Learned-Miller
We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet.
no code implementations • ICCV 2015 • Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?
Ranked #95 on 3D Point Cloud Classification on ModelNet40
no code implementations • CVPR 2014 • Andrew Kae, Benjamin Marlin, Erik Learned-Miller
In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos.
no code implementations • CVPR 2013 • Andrew Kae, Kihyuk Sohn, Honglak Lee, Erik Learned-Miller
Although the CRF is a good baseline labeler, we show how an RBM can be added to the architecture to provide a global shape bias that complements the local modeling provided by the CRF.