pwc-archive/papers-with-abstracts huggingface dataset:
Learning Graph Structure for Multi-Label Image Classification via Clique Generation
Authors: Mingkui Tan, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, Zhen Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, image-classification, Image Classification, Multi-Label Image Classification

Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.
Matrix Completion for Resolving Label Ambiguity
Authors: Ching-Hui Chen, Vishal M. Patel, Rama Chellappa
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Matrix Completion

In real applications, data is not always explicitly-labeled. For instance, label ambiguity exists when we associate two persons appearing in a news photo with two names provided in the caption. We propose a matrix completion-based method for predicting the actual labels from the ambiguously labeled instances, and a standard supervised classifier can learn from the disambiguated labels to classify new data. We further generalize the method to handle the labeling constraints between instances when such prior knowledge is available. Compared to existing methods, our approach achieves 2.9% improvement on the labeling accuracy of the Lost dataset and comparable performance on the Labeled Yahoo! News dataset.
Video Magnification in Presence of Large Motions
Authors: Mohamed Elgharib, Mohamed Hefeeda, Fredo Durand, William T. Freeman
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Matting

Video magnification reveals subtle variations that would be otherwise invisible to the naked eye. Current techniques require all motion in the video to be very small, which is unfortunately not always the case. Tiny yet meaningful motions are often combined with larger motions, such as the small vibrations of a gate as it rotates, or the microsaccades in a moving eye. We present a layer-based video magnification approach that can amplify small motions within large ones. An examined region/layer is temporally aligned and subtle variations are magnified. Matting is used to magnify only region of interest while maintaining integrity of nearby sites. Results show handling larger motions, larger amplification factors and significant reduction in artifacts over state of the art.
Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration
Authors: Mi Zhang, Jian Yao, Menghan Xia, Kai Li, Yi Zhang, Yaping Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images. In this paper, we propose an easily implemented fisheye image rectification algorithm with line constrains in the undistorted perspective image plane. A novel Multi-Label Energy Optimization (MLEO) method is adopted to merge short circular arcs sharing the same or the approximately same circular parameters and select long circular arcs for camera rectification. Further we propose an efficient method to estimate intrinsic parameters of the fisheye camera by automatically selecting three properly arranged long circular arcs from previously obtained circular arcs in the calibration procedure. Experimental results on a number of real images and simulated data show that the proposed method can achieve good results and outperforms the existing approaches and the commercial software in most cases.
Adaptive Eye-Camera Calibration for Head-Worn Devices
Authors: David Perra, Rohit Kumar Gupta, Jan-Michael Frahm
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Camera Calibration

We present a novel, continuous, locally optimal calibration scheme for use with head-worn devices.  Current calibration schemes solve for a globally optimal model of the eye-device transformation by performing calibration on a per-user or once-per-use basis.  However, these calibration schemes are impractical for real-world applications because they do not account for changes in calibration during the time of use.  Our calibration scheme allows a head-worn device to calculate a locally optimal eye-device transformation on demand by computing an optimal model from a local window of previous frames. By leveraging naturally occurring interest regions within the user's environment, our system can calibrate itself without the user's active participation.  Experimental results demonstrate that our proposed calibration scheme outperforms the existing state of the art systems while being significantly less restrictive to the user and the environment.
Modeling Object Appearance Using Context-Conditioned Component Analysis
Authors: Daniyar Turmukhambetov, Neill D. F. Campbell, Simon J. D. Prince, Jan Kautz
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Appearance Transfer, Object

Subspace models have been very successful at modeling the appearance of structured image datasets when the visual objects have been aligned in the images (e.g., faces). Even with extensions that allow for global transformations or dense warps of the image, the set of visual objects whose appearance may be modeled by such methods is limited. They are unable to account for visual objects where occlusion leads to changing visibility of different object parts (without a strict layered structure) and where a one-to-one mapping between parts is not preserved. For example bunches of bananas contain different numbers of bananas but each individual banana shares an appearance subspace.   In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared. This allows us to exploit the advantages of subspace appearance models with non-rigid, deformable objects whilst also dealing with complex occlusions and varying numbers of parts. We demonstrate the effectiveness of our new model with examples of structured inpainting and appearance transfer.
Displets: Resolving Stereo Ambiguities Using Object Knowledge
Authors: Fatma Guney, Andreas Geiger
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Semantic Segmentation

Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation. At time of submission, our approach ranks first across all KITTI stereo leaderboards.
Time-to-Contact From Image Intensity
Authors: Yukitoshi Watanabe, Fumihiko Sakaue, Jun Sato
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object

It is known that time-to-contact toward objects can be estimated just from changes in the object size in camera images, and we do not need any additional information, such as camera parameters and motions.  However, the existing methods for measuring the time-to-contact are based on geometric image features, such as corners and edge lines, and thus they cannot be used when there are no geometric features in images. In this paper, we propose a new method for computing the time-to-contact from photometric information in images. When a light source moves in the 3D scene, an observed intensity changes according to the motion of the light source.  In this paper, we analyze the change in photometric information in images, and show that the time-to-contact can be estimated just from the changes in intensity in images. Our method does not need any additional information, such as radiance of light source, reflectance of object and orientation of object surface. The proposed method can be used in various applications, such as vehicle driver assistance.
Robust Video Segment Proposals With Painless Occlusion Handling
Authors: Zhengyang Wu, Fuxin Li, Rahul Sukthankar, James M. Rehg
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Occlusion Handling

We propose a robust algorithm to generate video segment proposals. The proposals generated by our method can start from any frame in the video and are robust to complete occlusions. Our method does not assume specific motion models and even has a limited capability to generalize across videos. We build on our previous least squares tracking framework, where image segment proposals are generated and tracked using learned appearance models. The innovation in our new method lies in the use of two efficient moves, the merge move and free addition, to efficiently start segments from any frame and track them through complete occlusions, without much additional computation. Segment size interpolation is used for effectively detecting occlusions.  We propose a new metric for evaluating video segment proposals on the challenging VSB-100 benchmark and present state-of-the-art results. Preliminary results are also shown for the potential use of our framework to track segments across different videos.
Face Alignment Using Cascade Gaussian Process Regression Trees
Authors: Donghoon Lee, Hyunsin Park, Chang D. Yoo
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Alignment, regression

In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cGPRT) constructed by combining Gaussian process regression trees (GPRT) in a cascade stage-wise manner. Here, GPRT is a Gaussian process with a kernel defined by a set of trees. The kernel measures the similarity between two inputs as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization. Features for GPRT are designed using shape-indexed difference of Gaussian (DoG) filter responses sampled from local retinal patterns to increase stability and to attain robustness against geometric variances. Compared with the previous CRT-based face alignment methods that have shown state-of-the-art performances, cGPRT using shape-indexed DoG features performed best on the HELEN and 300-W datasets which are the most challenging dataset today.
Regularizing Max-Margin Exemplars by Reconstruction and Generative Models
Authors: Jose C. Rubio, Bjorn Ommer
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object Recognition

Part-based models are one of the leading paradigms in visual recognition. In the absence of costly part annotations, associating and aligning different training instances of a part classifier and finding characteristic negatives is challenging and computationally demanding. To avoid this costly mining of training samples, we estimate separate generative models for negatives and positives and integrate them into a max-margin exemplar-based model. The generative model and a sparsity constraint on the correlation between spatially neighboring feature dimensions regularize the part filters during learning and improve their generalization to similar instances. To suppress inappropriate positive part samples, we project the classifier back into the image domain and penalize against deviations from the original exemplar image patch. The part filter is then optimized to i) discriminate against clutter, to ii) generalize to similar instances of the part, and iii) to yield a good reconstruction of the original image patch. Moreover, we propose an approximation for estimating the geometric margin so that learning large numbers of parts becomes feasible. Experiments show improved part localization, object recognition, and part-based reconstruction performance compared to popular exemplar-based approaches on PASCAL VOC
A Fast Algorithm for Elastic Shape Distances Between Closed Planar Curves
Authors: Gunay Dogan, Javier Bernal, Charles R. Hagwood
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Effective computational tools for shape analysis are needed in many areas of science and engineering. We address this and propose a new fast iterative algorithm to compute the elastic geodesic distance between shapes of closed planar curves. The original algorithm for this has cubic time complexity with respect to the number of nodes per curve. Hence it is not suitable for large shape data sets. We aim for large-scale shape analysis and thus propose an iterative algorithm based on the original one but with quadratic time complexity. In practice, we observe subquadratic, almost linear running times, and that our algorithm scales very well with large numbers of nodes. The key to our algorithm is the decoupling of the optimization for the starting point and rotation from that of the reparametrization, and the development of fast dynamic programming and iterative nonlinear constrained optimization algorithms that work in tandem to compute optimal reparametrizations fast.
Reflection Removal for In-Vehicle Black Box Videos
Authors: Christian Simon, In Kyu Park
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Reflection Removal

In-vehicle black box camera becomes an popular equipment in many countries for security monitoring and event capturing. The readability of video contents is the most important capability, which is, however, often degraded due to the reflection on the windscreen. In this paper, we propose a novel method to remove the reflection on the windscreen in the in-vehicle black box videos. The main idea is to exploit spatio-temporal coherence of the reflection which shows that a vehicle moves forward while the reflection layer of internal object remains static. The average image prior is introduced by imposing a heavy-tail distribution with higher peak. The two layered scene is the base of the separation model. In order to remove the reflection, a cost non-convex function is developed based on this property and optimized. Experimental results demonstrate that the proposed approach successfully separates the layers in real black box videos.
Tree Quantization for Large-Scale Similarity Search and Classification
Authors: Artem Babenko, Victor Lempitsky
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification, image-classification, Image Classification, Quantization, Retrieval

We propose a new vector encoding scheme (tree quantization) that obtains lossy compact codes for high-dimensional vectors via tree-based dynamic programming. Similarly to several previous schemes such as product quantization, these codes correspond to codeword numbers within multiple codebooks. We propose an integer programming-based optimization that jointly recovers the coding tree structure and the codebooks by minimizing the compression error on a training dataset. In the experiments with diverse visual descriptors (SIFT, neural codes, Fisher vectors), tree quantization is shown to combine fast encoding and state-of-the-art accuracy in terms of the compression error, the retrieval performance, and the image classification error.
Integrating Parametric and Non-Parametric Models For Scene Labeling
Authors: Bing Shuai, Gang Wang, Zhen Zuo, Bing Wang, Lifan Zhao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Metric Learning, Scene Labeling

We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by putting a global scene constraint. We estimate the global potential in a non-parametric framework. Furthermore, a large margin based CNN metric learning method is proposed for better global potential estimation. The final pixel class prediction is performed by integrating local and global beliefs. Even without any post-processing, we achieve state-of-the-art on SiftFlow and competitive results on Stanford Background benchmark.
Mining Semantic Affordances of Visual Object Categories
Authors: Yu-Wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Collaborative Filtering, Object

Affordances are fundamental attributes of objects. Affordances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding affordances is crucial for recognizing human activities in visual data and for robots to interact with the world.  In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it. This is equivalent to connecting verb nodes and noun nodes in WordNet, or filling an affordance matrix encoding the plausibility of each action-object pair. We introduce a new benchmark with crowdsourced ground truth affordances on 20 PASCAL VOC object classes and 957 action classes.  We explore a number of approaches including text mining, visual mining, and collaborative filtering. Our analyses yield a number of significant insights that reveal the most effective ways of collecting knowledge of semantic affordances.
Causal Video Object Segmentation From Persistence of Occlusions
Authors: Brian Taylor, Vasiliy Karasev, Stefano Soatto
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Occlusion relations inform the partition of the image domain into ``objects'' but are difficult to determine from a single image or short-baseline video. We show how long-term occlusion relations can be robustly inferred from video, and used within a convex optimization framework to segment the image domain into regions. We highlight the challenges in determining these occluder/occluded relations and ensuring regions remain temporally consistent, propose strategies to overcome them, and introduce an efficient numerical scheme to perform the partition directly on the pixel grid, without the need for superpixelization or other preprocessing steps.
Multiple Instance Learning for Soft Bags via Top Instances
Authors: Weixin Li, Nuno Vasconcelos
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Instance Learning

A generalized formulation of the multiple instance learning problem is considered. Under this formulation, both positive and negative bags are soft, in the sense that negative bags can also contain positive instances. This reflects a problem setting commonly found in practical applications, where labeling noise appears on both positive and negative training samples. A novel bag-level representation is introduced, using instances that are most  likely to be positive (denoted top instances), and its ability to separate soft bags, depending on their relative composition in terms of positive and negative instances, is studied. This study inspires a new large-margin algorithm for soft-bag classification, based on a latent support vector machine that efficiently explores the combinatorial space of bag compositions. Empirical evaluation on three datasets is shown to confirm the main findings of the theoretical analysis and the effectiveness of the  proposed soft-bag classifier.
Multiclass Semantic Video Segmentation With Object-Level Active Inference
Authors: Buyu Liu, Xuming He
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

We address the problem of integrating object reasoning with supervoxel labeling in multiclass semantic video segmentation. To this end, we first propose an object-augmented dense CRF in spatio-temporal domain, which captures long-range dependency between supervoxels, and imposes consistency between object and supervoxel labels. We develop an efficient mean field inference algorithm to jointly infer the supervoxel labels, object activations and their occlusion relations for a moderate number of object proposals.    To scale up our method, we adopt an active inference strategy to improve the efficiency, which adaptively selects object subgraphs in the object-augmented dense CRF. We formulate the problem as a Markov Decision Process, which learns an approximate optimal policy based on a reward of accuracy improvement and a set of well-designed model and input features. We evaluate our method on three publicly available multiclass video semantic segmentation datasets and demonstrate superior efficiency and accuracy.
Weakly Supervised Localization of Novel Objects Using Appearance Transfer
Authors: Mrigank Rochan, Yang Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Appearance Transfer, Object

We consider the problem of localizing unseen objects in weakly labeled image collections. Given a set of images annotated at the image level, our goal is to localize the object in each image. The novelty of our proposed work is in addition to building object appearance model from the weakly labeled data, we also make use of existing detectors for some other object classes (which we call ``familiar objects''). We propose a method for transferring the appearance models of the familiar objects to the unseen object. Our experimental results on both image and video datasets demonstrate the effectiveness of our approach.
First-Person Pose Recognition Using Egocentric Workspaces
Authors: Gregory Rogez, James S. Supancic III, Deva Ramanan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Pose Estimation

We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint priors and depth-based features are crucial for robust performance. In egocentric views, hands and arms are observable within a well defined volume in front of the camera. We call this volume an egocentric workspace. A notable property is that hand appearance correlates with workspace location. To exploit this correlation, we classify arm+hand configurations in a global egocentric coordinate frame, rather than a local scanning window. This greatly simplify the architecture and improves performance. We propose an efficient pipeline which 1) generates synthetic  workspace exemplars for training using a virtual chest-mounted camera whose intrinsic parameters match our physical camera, 2) computes perspective-aware depth features on this entire volume and 3) recognizes discrete arm+hand pose classes through a sparse multi-class SVM. We achieve state-of-the-art hand pose recognition performance from egocentric RGB-D images in real-time.