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Article
Open AccessOccluded Video Instance Segmentation: A Benchmark
Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, th...
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Chapter and Conference Paper
Deep Fundamental Matrix Estimation Without Correspondences
Estimating fundamental matrices is a classic problem in computer vision. Traditional methods rely heavily on the correctness of estimated key-point correspondences, which can be noisy and unreliable. As a resu...
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Chapter and Conference Paper
Learning Single-View 3D Reconstruction with Limited Pose Supervision
It is expensive to label images with 3D structure or precise camera pose. Yet, this is precisely the kind of annotation required to train single-view 3D reconstruction models. In contrast, unlabeled images or ...
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Chapter and Conference Paper
Multimodal Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding image...
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Chapter and Conference Paper
Convolutional Networks with Adaptive Inference Graphs
Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained d...
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Article
Editorial: Special Issue on Active and Interactive Methods in Computer Vision
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Chapter and Conference Paper
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This ...
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Article
Open AccessGlobally Optimal Algorithms for Stratified Autocalibration
We present practical algorithms for stratified autocalibration with theoretical guarantees of global optimality. Given a projective reconstruction, we first upgrade it to affine by estimating the position of t...
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Chapter and Conference Paper
Word Spotting in the Wild
We present a method for spotting words in the wild, i.e., in real images taken in unconstrained environments. Text found in the wild has a surprising range of difficulty. At one end of the spectrum, Optical Chara...
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Chapter and Conference Paper
Visual Recognition with Humans in the Loop
We present an interactive, hybrid human-computer method for object classification. The method applies to classes of objects that are recognizable by people with appropriate expertise (e.g., animal species or airp...
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Chapter and Conference Paper
Multiple Component Learning for Object Detection
Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates...
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Chapter and Conference Paper
Weakly Supervised Object Localization with Stable Segmentations
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from we...
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Chapter and Conference Paper
Practical Global Optimization for Multiview Geometry
This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography estim...
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Chapter and Conference Paper
On Refractive Optical Flow
This paper presents a novel generalization of the optical flow equation to the case of refraction, and it describes a method for recovering the refractive structure of an object from a video sequence acquired ...
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Chapter and Conference Paper
A Feature-Based Approach for Determining Dense Long Range Correspondences
Planar motion models can provide gross motion estimation and good segmentation for image pairs with large inter-frame disparity. However, as the disparity becomes larger, the resulting dense correspondences wi...
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Chapter and Conference Paper
Spectral Partitioning with Indefinite Kernels Using the Nyström Extension
Fowlkes et al. [7] recently introduced an approximation to the Normalized Cut (NCut) grou** algorithm [18] based on random subsampling and the Nyström extension. As presented, their method is restricted to the ...
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Chapter and Conference Paper
Approximate Thin Plate Spline Map**s
The thin plate spline (TPS) is an effective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inve...
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Chapter and Conference Paper
Finding boundaries in natural images: A new method using point descriptors and area completion
We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their resp...
