pwc-archive/papers-with-abstracts huggingface dataset:
Interpolation on the Manifold of K Component GMMs
Authors: Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Probability density functions (PDFs) are fundamental "objects" in mathematics with numerous applications in computer vision, machine learning and medical imaging. The feasibility of basic operations such as computing the distance between two PDFs and estimating a mean of a set of PDFs is a direct function of the representation we choose to work with. In this paper, we study the Gaussian mixture model  (GMM) representation of the PDFs motivated by its numerous attractive features. (1) GMMs are arguably more interpretable than, say, square root parameterizations (2) the model complexity can be explicitly controlled by the number of components and (3) they are already widely used in many applications. The main contributions of  this paper are numerical algorithms to enable basic operations on such objects that strictly respect their underlying geometry.  For instance, when operating with a set of k component GMMs,  a first order expectation is that the result of  simple operations like interpolation and averaging should  provide an object that is also a k component GMM.  The literature provides very little guidance on enforcing such  requirements systematically. It turns out that these tasks are  important internal modules for analysis and processing of a field of ensemble average propagators (EAPs), common  in diffusion weighted magnetic resonance imaging. We provide proof of principle experiments showing how the proposed algorithms for interpolation can facilitate statistical  analysis of such data, essential to many neuroimaging studies. Separately, we also derive interesting connections of our algorithm  with functional spaces of Gaussians, that may be of independent interest.
Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple
Authors: Oren Freifeld, Soren Hauberg, Kayhan Batmanghelich, John W. Fisher III
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Registration

We propose novel finite-dimensional spaces of R - R transformations, n [?] 1, 2, 3, derived from (continuously-defined) parametric stationary velocity fields. Particularly, we obtain these transformations, which are diffeomorphisms, by fast and highly-accurate integration of continuous piecewise-affine velocity fields; we also provide an exact solution for n = 1. The simple-yet-highly-expressive proposed representation handles optional constraints (e.g., volume preservation) easily and supports convenient modeling choices and rapid likelihood evaluations (facilitating tractable inference over latent transformations). Its applications include, but are not limited to: unconstrained optimization over monotonic functions; modeling cumulative distribution functions or histograms; time warping; image registration; landmark-based warping; real-time diffeomorphic image editing. Our code is available at https://github.com/freifeld/cpabDiffeo
Entropy-Based Latent Structured Output Prediction
Authors: Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Prediction, Structured Prediction

Recently several generalizations of the popular latent structural SVM framework have been proposed in the literature. Broadly speaking, the generalizations can be divided into two categories: (i) those that predict the output variables while either marginalizing the latent variables or estimating their most likely values; and (ii) those that predict the output variables by minimizing an entropy-based uncertainty measure over the latent space. In order to aid their application in computer vision, we study these generalizations with the aim of identifying their strengths and weaknesses. To this end, we propose a novel prediction criterion that includes as special cases all previous prediction criteria that have been used in the literature. Specifically, our framework's prediction criterion minimizes the Aczel and Daroczy entropy of the output. This in turn allows us to design a learning objective that provides a unified framework (UF) for latent structured prediction. We develop a single optimization algorithm and empirically show that it is as effective as the more complex approaches that have been previously employed for latent structured prediction. Using this algorithm, we provide empirical evidence that lends support to prediction via the minimization of the latent space uncertainty.
Fast Orthogonal Projection Based on Kronecker Product
Authors: Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shi-Fu Chang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval, Quantization

We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications. In this, a structured matrix is formed by the Kronecker product of a series of smaller orthogonal matrices. This achieves O(dlogd) computational complexity and O(logd) space complexity for d-dimensional data, a drastic improvement over the standard unstructured projections whose computational and space complexities are both O(d^2). The proposed structured matrices are applicable to a number of application domains, and are faster and more compact than other structured matrices used in the past. We also introduce an efficient learning procedure for optimizing such matrices in a data dependent fashion. We demonstrate the significant advantages of the proposed approach in solving the approximate nearest neighbor (ANN) image search problem with both binary embedding and quantization. We find that the orthogonality plays a very important role in solving ANN problem, since the random orthogonal Kronecker projection has already provided promising performance. Comprehensive experiments show that the proposed approach can achieve similar or better accuracy as the existing state-of-the-art but with significantly less time and memory.
Predicting Multiple Structured Visual Interpretations
Authors: Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Pose Estimation, Scene Segmentation, Segmentation

We present a simple approach for producing a small number of structured visual outputs which have high recall, for a variety of tasks including monocular pose estimation and semantic scene segmentation. Current state-of-the-art approaches learn a single model and modify inference procedures to produce a small number of diverse predictions. We take the alternate route of modifying the learning procedure to directly optimize for good, high recall sequences of structured-output predictors. Our approach introduces no new parameters, naturally learns diverse predictions and is not tied to any specific structured learning or inference procedure. We leverage recent advances in the contextual submodular maximization literature to learn a sequence of predictors and empirically demonstrate the simplicity and performance of our approach on multiple challenging vision tasks including achieving state-of-the-art results on multiple predictions for monocular pose-estimation and image foreground/background segmentation.
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
Authors: Chunshui Cao, Xian-Ming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas S. Huang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections. In this paper, we will briefly introduce the background of feedbacks in the human visual cortex, which motivates us to develop a computational feedback mechanism in the deep neural networks. The proposed networks perform inference from image features in a bottom-up manner as traditional convolutional networks; while during feedback loops it sets up high-level semantic labels as the agoala to infer the activation status of hidden layer neurons. The feedback networks help us better visualize and understand on how deep neural networks work as well as capture visual attention on expected objects, even in the images with cluttered background and multiple objects.
Matrix Backpropagation for Deep Networks With Structured Layers
Authors: Catalin Ionescu, Orestis Vantzos, Cristian Sminchisescu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fields, and from the simplicity and scalability of the gradient-descent training procedure based on backpropagation. An open problem is the inclusion of layers that perform global, structured matrix computations like segmentation (e.g. normalized cuts) or higher-order pooling (e.g. log-tangent space metrics defined over the manifold of symmetric positive definite matrices) while preserving the validity and efficiency of an end-to-end deep training framework. In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures. At the heart of our methodology is the development of the theory and practice of backpropagation that generalizes to the calculus of adjoint matrix variations. We perform segmentation experiments using the BSDS and MSCOCO  benchmarks and demonstrate that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take  advantage of such global layers.
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition
Authors: Heechul Jung, Sihaeng Lee, Junho Yim, Sunjeong Park, Junmo Kim
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Temporal information has useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal appearance features from image sequences, while the other deep network extracts temporal geometry features from temporal facial landmark points. These two models are combined using a new integration method in order to boost the performance of the facial expression recognition. Through several experiments, we show that the two models cooperate with each other. As a result, we achieve superior performance to other state-of-the-art methods in the CK+ and Oulu-CASIA databases. Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.
Face Flow
Authors: Patrick Snape, Anastasios Roussos, Yannis Panagakis, Stefanos Zafeiriou
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Optical Flow Estimation

In this paper, we propose a method for the robust and efficient computation of  multi-frame optical flow in an expressive sequence of facial images. We formulate a novel energy minimisation problem for establishing dense  correspondences between a neutral template and every frame of a sequence. We exploit the highly correlated nature of human expressions by representing dense facial motion using a deformation basis. Furthermore, we exploit the even higher correlation between deformations in  a given input sequence by imposing a low-rank prior on the coefficients of the  deformation basis, yielding temporally consistent optical flow. Our proposed model-based formulation, in conjunction with the inverse  compositional strategy and low-rank matrix optimisation that we adopt, leads to  a highly efficient algorithm for calculating facial flow.  As experimental evaluation, we show quantitative experiments on a challenging  novel benchmark of face sequences, with dense ground truth optical flow provided by motion  capture data. We also provide qualitative results on a real sequence displaying fast motion and occlusions. Extensive quantitative and qualitative comparisons demonstrate that the proposed method  outperforms state-of-the-art optical flow and dense non-rigid registration techniques,  whilst running an order of magnitude faster.
Discriminative Low-Rank Tracking
Authors: Yao Sui, Yafei Tang, Li Zhang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Good tracking performance is in general attributed to accurate representation over previously obtained targets or reliable discrimination between the target and the surrounding background. In this work, we exploit the advantages of the both approaches to achieve a robust tracker. We construct a subspace to represent the target and the neighboring background, and simultaneously propagate their class labels via the learned subspace. Moreover, we propose a novel criterion to identify the target from numerous target candidates on each frame, which takes into account both discrimination reliability and representation accuracy. In addition, with the proposed criterion, the ambiguity in the class labels of the neighboring background samples, which often influences the reliability of discriminative tracking model, is effectively alleviated, while the training set is still kept small. Extensive experiments demonstrate that our tracker performs favourably against many other state-of-the-art trackers.
SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking
Authors: Han-Ul Kim, Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Visual Tracking

A simple yet effective object descriptor for visual tracking is proposed in this paper. We first decompose the bounding box of a target object into multiple patches, which are described by color and gradient histograms. Then, we concatenate the features of the spatially ordered patches to represent the object appearance. Moreover, to alleviate the impacts of background information possibly included in the bounding box, we determine patch weights using random walk with restart (RWR) simulations. The patch weights represent the importance of each patch in the description of foreground information, and are used to construct an object descriptor, called spatially ordered and weighted patch (SOWP) descriptor. We incorporate the proposed SOWP descriptor into the structured output tracking framework. Experimental results demonstrate that the proposed algorithm yields significantly better performance than the state-of-the-art trackers on a recent benchmark dataset, and also excels in another recent benchmark dataset.
Live Repetition Counting
Authors: Ofir Levy, Lior Wolf
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The task of counting the number of repetitions of approximately the same action in an input video sequence is addressed. The proposed method runs online and not on the complete pre-captured video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a convolutional neural network and the information is then integrated over time. The entropy of the network's predictions is used in order to automatically start and stop the repetition counter and to select the appropriate time scale. Coupled with a region of interest detection mechanism, the method is robust enough to handle real world videos, even when the camera is moving. A unique property of our method is that it is shown to successfully train on entirely unrealistic data created by synthesizing moving random patches.
Multi-Kernel Correlation Filter for Visual Tracking
Authors: Ming Tang, Jiayi Feng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Visual Tracking

Correlation filter based trackers are ranked top in terms of performances. Nevertheless, they only employ a single kernel at a time. In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features to further improve the performance. Moreover, it may easily introduce location and representation errors to search several discrete scales for the proper one of the object bounding box, because normally the discrete candidate scales are determined and the corresponding feature pyramid are generated ahead of searching. In this paper, we will propose a novel and efficient scale estimation method based on optimal bisection search and fast evaluation of features. Our scale estimation method is the first one that uses the truly minimal number of layers of feature pyramid and avoids constructing the pyramid before searching for proper scales.
Joint Probabilistic Data Association Revisited
Authors: Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
Tracking-by-Segmentation With Online Gradient Boosting Decision Tree
Authors: Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Segmentation

We propose an online tracking algorithm that adaptively models target appearances based on an online gradient boosting decision tree. Our algorithm is particularly useful for non-rigid and/or articulated objects since it handles various deformations of the target effectively by integrating a classifier operating on individual patches and provides segmentation masks of the target as final results. The posterior of the target state is propagated over time by particle filtering, where the likelihood is computed based mainly on patch-level confidence map associated with a latent target state corresponding to each sample. Once tracking is completed in each frame, our gradient boosting decision tree is updated to adapt new data in a recursive manner. For effective evaluation of segmentation-based tracking algorithms, we construct a new ground-truth that contains pixel-level annotation of segmentation mask. We evaluate the performance of our tracking algorithm based on the measures for segmentation masks, where our algorithm illustrates superior accuracy compared to the state-of-the-art segmentation-based tracking methods.
Exploring Causal Relationships in Visual Object Tracking
Authors: Karel Lebeda, Simon Hadfield, Richard Bowden
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Tracking, Visual Object Tracking

Causal relationships can often be found in visual object tracking between the motions of the camera and that of the tracked object. This object motion may be an effect of the camera motion, e.g. an unsteady handheld camera. But it may also be the cause, e.g. the cameraman framing the ob- ject. In this paper we explore these relationships, and pro- vide statistical tools to detect and quantify them; these are based on transfer entropy and stem from information the- ory. The relationships are then exploited to make predic- tions about the object location. The approach is shown to be an excellent measure for describing such relationships. On the VOT2013 dataset the prediction accuracy is increased by 62 % over the best non-causal predictor. We show that the location predictions are robust to camera shake and sud- den motion, which is invaluable for any tracking algorithm and demonstrate this by applying causal prediction to two state-of-the-art trackers. Both of them benefit, Struck gain- ing a 7 % accuracy and 22 % robustness increase on the VTB1.1 benchmark, becoming the new state-of-the-art.
Hierarchical Convolutional Features for Visual Tracking
Authors: Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition, Object Tracking, Visual Object Tracking, Visual Tracking

Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.
Robust Non-Rigid Motion Tracking and Surface Reconstruction Using L0 Regularization
Authors: Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, Qionghai Dai
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Surface Reconstruction

We present a new motion tracking method to robustly reconstruct non-rigid geometries and motions from single view depth inputs captured by a consumer depth sensor. The idea comes from the observation of the existence of intrinsic articulated subspace in most of non-rigid motions. To take advantage of this characteristic, we propose a novel L0 based motion regularizer with an iterative optimization solver that can implicitly constrain local deformation only on joints with articulated motions, leading to reduced solution space and physical plausible deformations. The L0 strategy is integrated into the available non-rigid motion tracking pipeline, forming the proposed L0-L2 non-rigid motion tracking method that can adaptively stop the tracking error propagation. Extensive experiments over complex human body motions with occlusions, face and hand motions demonstrate that our approach substantially improves tracking robustness and surface reconstruction accuracy.