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Situation recognition: Visual semantic role labeling for image understanding

Situation recognition: Visual semantic role labeling for ima...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yatskar, Mark Zettlemoyer, Luke Farhadi, Ali Computer Science and Engineering University of Washington SeattleWA United States SeattleWA United States

ISBN: (纸本)9781467388511

This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e.g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a large space of possible situations and collect a large-scale dataset containing over 500 activities, 1,700 roles, 11,000 objects, 125,000 images, and 200,000 unique situations. We also introduce structured prediction baselines and show that, in activity-centric images, situation-driven prediction of objects and activities outperforms independent object and activity recognition.

关键词： computer vision

来源：评论

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Subspace clustering with priors via sparse quadratically constrained quadratic programming

Subspace clustering with priors via sparse quadratically con...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cheng, Yongfang Wang, Yin Sznaier, Mario Camps, Octavia Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781467388511

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi-definite optimization problem subject to an additional rank constrain that involves only a very small number of variables. This is established by first reducing the problem to a quadratically constrained quadratic problem and then using its special structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact. When combined with the standard nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples.

关键词： pattern recognition

来源：评论

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Predicting motivations of actions by leveraging text

Predicting motivations of actions by leveraging text

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Vondrick, Carl Oktay, Deniz Pirsiavash, Hamed Torralba, Antonio Massachusetts Institute of Technology United States University of Maryland Baltimore County United States

ISBN: (纸本)9781467388511

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.

关键词： computer vision

来源：评论

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Simultaneous clustering and model selection for tensor affinities

Simultaneous clustering and model selection for tensor affin...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Li, Zhuwen Yang, Shuoguang Cheong, Loong-Fah Toh, Kim-Chuan National University of Singapore Singapore Columbia University United States

ISBN: (纸本)9781467388511

Estimating the number of clusters remains a difficult model selection problem. We consider this problem in the domain where the affinity relations involve groups of more than two nodes. Building on the previous formulation for the pairwise affinity case, we exploit the mathematical structures in the higher order case. We express the original minimal-rank and positive semi-definite (PSD) constraints in a form amenable for numerical implementation, as the original constraints are either intractable or even undefined in general in the higher order case. To scale to large problem sizes, we also propose an alternative formulation, so that it can be efficiently solved via stochastic optimization in an online fashion. We evaluate our algorithm with different applications to demonstrate its superiority, and show it can adapt to varying levels of unbalancedness of clusters.

关键词： computer vision

来源：评论

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Bilateral space video segmentation

Bilateral space video segmentation

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Märki, Nicolas Perazzi, Federico Wang, Oliver Sorkine-Hornung, Alexander ETH Zurich Switzerland Disney Research United States Adobe Systems Inc. United States

ISBN: (纸本)9781467388511

In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.

关键词： computer vision

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Neural module networks

Neural module networks

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Andreas, Jacob Rohrbach, Marcus Darrell, Trevor Klein, Dan University of California Berkeley United States

ISBN: (纸本)9781467388511

Visual question answering is fundamentally compositional in nature-a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.

关键词： computer vision

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Automated 3D face reconstruction from multiple images using quality measures

Automated 3D face reconstruction from multiple images using ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Piotraschke, Marcel Blanz, Volker Institute for Vision and Graphics University of Siegen Germany

ISBN: (纸本)9781467388511

Automated 3D reconstruction of faces from images is challenging if the image material is difficult in terms of pose, lighting, occlusions and facial expressions, and if the initial 2D feature positions are inaccurate or unreliable. We propose a method that reconstructs individual 3D shapes from multiple single images of one person, judges their quality and then combines the best of all results. This is done separately for different regions of the face. The core element of this algorithm and the focus of our paper is a quality measure that judges a reconstruction without information about the true shape. We evaluate different quality measures, develop a method for combining results, and present a complete processing pipeline for automated reconstruction.

关键词： Image reconstruction

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Unsupervised cross-dataset transfer learning for person re-identification

Unsupervised cross-dataset transfer learning for person re-i...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Peng, Peixi Xiang, Tao Wang, Yaowei Pontil, Massimiliano Gong, Shaogang Huang, Tiejun Tian, Yonghong National Engineering Laboratory for Video Technology Peking University Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electronic Engineering Beijing Institute of Technology China Italian Institute of Technology Italy Cooperative Medianet Innovation Center China

ISBN: (纸本)9781467388511

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task dictionary learning method which is able to learn a dataset-shared but targetdata-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.

关键词： pattern recognition

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Dense monocular depth estimation in complex dynamic scenes

Dense monocular depth estimation in complex dynamic scenes

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ranftl, René Vineet, Vibhav Chen, Qifeng Koltun, Vladlen Intel Labs United States Stanford University United States

ISBN: (纸本)9781467388511

We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene. The approach produces a dense depth map from two consecutive frames. Moving objects are reconstructed along with the surrounding environment. We provide a novel motion segmentation algorithm that segments the optical flow field into a set of motion models, each with its own epipolar geometry. We then show that the scene can be reconstructed based on these motion models by optimizing a convex program. The optimization jointly reasons about the scales of different objects and assembles the scene in a common coordinate frame, determined up to a global scale. Experimental results demonstrate that the presented approach outperforms prior methods for monocular depth estimation in dynamic scenes.

关键词： computer vision

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Fast temporal activity proposals for efficient detection of human actions in untrimmed videos

Fast temporal activity proposals for efficient detection of ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Heilbron, Fabian Caba Niebles, Juan Carlos Ghanem, Bernard Saudi Arabia Department of Computer Science Stanford University United States Universidad del Norte Colombia

ISBN: (纸本)9781467388511

In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored. This is in part due to the computational complexity of current action recognition approaches and the lack of a method that proposes fewer intervals in the video, where activity processing can be focused. In this paper, we introduce a proposal method that aims to recover temporal segments containing actions in untrimmed videos. Building on techniques for learning sparse dictionaries, we introduce a learning framework to represent and retrieve activity proposals. We demonstrate the capabilities of our method in not only producing high quality proposals but also in its efficiency. Finally, we show the positive impact our method has on recognition performance when it is used for action detection, while running at 10FPS.

关键词： computer vision

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