Search Results for author: Erik Learned-Miller

Found 35 papers, 11 papers with code

Passage Retrieval for Outside-Knowledge Visual Question Answering

1 code implementation9 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.

Image Captioning Passage Retrieval +2

Universal Off-Policy Evaluation

no code implementations26 Apr 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.

Decision Making

DCVNet: Dilated Cost Volume Networks for Fast Optical Flow

no code implementations31 Mar 2021 Huaizu Jiang, Erik Learned-Miller

To address this, a sequential strategy is usually adopted, where correspondence sampling in a local neighborhood with a small radius suffices.

Optical Flow Estimation

SID-NISM: A Self-supervised Low-light Image Enhancement Framework

no code implementations16 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.

Low-Light Image Enhancement

Shot in the Dark: Few-Shot Learning with No Base-Class Labels

no code implementations6 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.

Few-Shot Learning Self-Supervised Learning

Improving Face Recognition by Clustering Unlabeled Faces in the Wild

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.

Face Clustering Face Recognition +2

Cross-Supervised Object Detection

no code implementations26 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.

Object Detection

In Defense of Grid Features for Visual Question Answering

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).

Image Captioning Question Answering +1

SENSE: a Shared Encoder Network for Scene-flow Estimation

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.

Disparity Estimation Occlusion Estimation +3

A New Confidence Interval for the Mean of a Bounded Random Variable

no code implementations15 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.

Automatic adaptation of object detectors to new domains using self-training

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.

Knowledge Distillation Pedestrian Detection +1

Pixel-Adaptive Convolutional Neural Networks

1 code implementation 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.

Nonparametric Curve Alignment

no code implementations2 Feb 2019 Marwan Mattar, Michael Ross, Erik Learned-Miller

Congealing is a flexible nonparametric data-driven framework for the joint alignment of data.

Unsupervised Hard Example Mining from Videos for Improved Object Detection

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.

Face Detection Object Detection +1

The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation

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.

Motion Segmentation Semantic Segmentation

End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering

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.

Face Detection

End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering

no code implementations7 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.

Face Detection

A Detailed Rubric for Motion Segmentation

no code implementations31 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.

Motion Segmentation

Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks

no code implementations13 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.

Image Classification

Face Detection with the Faster R-CNN

1 code implementation10 Jun 2016 Huaizu Jiang, Erik Learned-Miller

The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks.

Face Detection Object Detection

It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos

no code implementations1 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.

Motion Segmentation Optical Flow Estimation

Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations

no code implementations5 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.

Motion Segmentation Optical Flow Estimation

Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space

no code implementations5 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].

Background subtraction - separating the modeling and the inference

no code implementations5 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.

One-to-many face recognition with bilinear CNNs

no code implementations3 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.

Face Detection Face Model +1

Multi-view Convolutional Neural Networks for 3D Shape Recognition

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?

3D Point Cloud Classification 3D Shape Recognition

The Shape-Time Random Field for Semantic Video Labeling

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.

Motion Capture

Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling

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.

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