mtybilly/Instruct2DS huggingface dataset:
How Related Exemplars Help Complex Event Detection in Web Videos?
Authors: Yi Yang, Zhigang Ma, Zhongwen Xu, Shuicheng Yan, Alexander G. Hauptmann
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Compared to visual concepts such as actions, scenes and objects, complex event is a higher level abstraction of longer video sequences. For example, a "marriage proposal" event is described by multiple objects (e.g., ring, faces), scenes (e.g., in a restaurant, outdoor) and actions (e.g., kneeling down). The positive exemplars which exactly convey the precise semantic of an event are hard to obtain. It would be beneficial to utilize the related exemplars for complex event detection. However, the semantic correlations between related exemplars and the target event vary substantially as relatedness assessment is subjective. Two related exemplars can be about completely different events, e.g., in the TRECVID MED dataset, both bicycle riding and equestrianism are labeled as related to "attempting a bike trick" event. To tackle the subjectiveness of human assessment, our algorithm automatically evaluates how positive the related exemplars are for the detection of an event and uses them on an exemplar-specific basis. Experiments demonstrate that our algorithm is able to utilize related exemplars adaptively, and the algorithm gains good performance for complex event detection.
Decomposing Bag of Words Histograms
Authors: Ankit Gandhi, Karteek Alahari, C.V. Jawahar
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We aim to decompose a global histogram representation of an image into histograms of its associated objects and regions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively discriminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects. We evaluate our method on a wide variety of composite histograms, and also compare it with MRF -based solutions. In addition to merely measuring the accuracy of decomposition, we also show the utility of the estimated object and background histograms for the task of image classification on the PASCAL VOC 2007 dataset.
Higher Order Matching for Consistent Multiple Target Tracking
Authors: Chetan Arora, Amir Globerson
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper addresses the data assignment problem in multi frame multi object tracking in video sequences. Traditional methods employing maximum weight bipartite matching offer limited temporal modeling. It has recently been shown [6, 8, 24] that incorporating higher order temporal constraints improves the assignment solution. Finding maximum weight matching with higher order constraints is however NP-hard and the solutions proposed until now have either been greedy [8] or rely on greedy rounding of the solution obtained from spectral techniques [15]. We propose a novel algorithm to find the approximate solution to data assignment problem with higher order temporal constraints using the method of dual decomposition and the MPLP message passing algorithm [21]. We compare the proposed algorithm with an implementation of [8] and [15] and show that proposed technique provides better solution with a bound on approximation factor for each inferred solution.
Complementary Projection Hashing
Authors: Zhongming Jin, Yao Hu, Yue Lin, Debing Zhang, Shiding Lin, Deng Cai, Xuelong Li
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2 c buckets, where c is the length of the hash code. A good hashing method should satisfy the following two requirements: 1) mapping the nearby data points into the same bucket or nearby (measured by the Hamming distance) buckets. 2) all the data points are evenly distributed among all the buckets. In this paper, we propose a novel algorithm named Complementary Projection Hashing (CPH) to find the optimal hashing functions which explicitly considers the above two requirements. Specifically, CPH aims at sequentially finding a series of hyperplanes (hashing functions) which cross the sparse region of the data. At the same time, the data points are evenly distributed in the hypercubes generated by these hyperplanes. The experiments comparing with the state-of-the-art hashing methods demonstrate the effectiveness of the proposed method.
Super-resolution via Transform-Invariant Group-Sparse Regularization
Authors: Carlos Fernandez-Granda, Emmanuel J. Candès
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a framework to super-resolve planar regions found in urban scenes and other man-made environments by taking into account their 3D geometry. Such regions have highly structured straight edges, but this prior is challenging to exploit due to deformations induced by the projection onto the imaging plane. Our method factors out such deformations by using recently developed tools based on convex optimization to learn a transform that maps the image to a domain where its gradient has a simple group-sparse structure. This allows to obtain a novel convex regularizer that enforces global consistency constraints between the edges of the image. Computational experiments with real images show that this data-driven approach to the design of regularizers promoting transform-invariant group sparsity is very effective at high super-resolution factors. We view our approach as complementary to most recent superresolution methods, which tend to focus on hallucinating high-frequency textures.
Inferring "Dark Matter" and "Dark Energy" from Videos
Authors: Dan Xie, Sinisa Todorovic, Song-Chun Zhu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper presents an approach to localizing functional objects in surveillance videos without domain knowledge about semantic object classes that may appear in the scene. Functional objects do not have discriminative appearance and shape, but they affect behavior of people in the scene. For example, they "attract" people to approach them for satisfying certain needs (e.g., vending machines could quench thirst), or "repel" people to avoid them (e.g., grass lawns). Therefore, functional objects can be viewed as "dark matter", emanating "dark energy" that affects people's trajectories in the video. To detect "dark matter" and infer their "dark energy" field, we extend the Lagrangian mechanics. People are treated as particle-agents with latent intents to approach "dark matter" and thus satisfy their needs, where their motions are subject to a composite "dark energy" field of all functional objects in the scene. We make the assumption that people take globally optimal paths toward the intended "dark matter" while avoiding latent obstacles. A Bayesian framework is used to probabilistically model: people's trajectories and intents, constraint map of the scene, and locations of functional objects. A data-driven Markov Chain Monte Carlo (MCMC) process is used for inference. Our evaluation on videos of public squares and courtyards demonstrates our effectiveness in localizing functional objects and predicting people's trajectories in unobserved parts of the video footage.
Optimal Orthogonal Basis and Image Assimilation: Motion Modeling
Authors: Etienne Huot, Giuseppe Papari, Isabelle Herlin
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper describes modeling and numerical computation of orthogonal bases, which are used to describe images and motion fields. Motion estimation from image data is then studied on subspaces spanned by these bases. A reduced model is obtained as the Galerkin projection on these subspaces of a physical model, based on Euler and optical flow equations. A data assimilation method is studied, which assimilates coefficients of image data in the reduced model in order to estimate motion coefficients. The approach is first quantified on synthetic data: it demonstrates the interest of model reduction as a compromise between results quality and computational cost. Results obtained on real data are then displayed so as to illustrate the method.
Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?
Authors: Olga Russakovsky, Jia Deng, Zhiheng Huang, Alexander C. Berg, Li Fei-Fei
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. [10] on the standard PASCAL VOC detection dataset, we perform a large-scale study on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent testbed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of imagelevel and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.
Neighbor-to-Neighbor Search for Fast Coding of Feature Vectors
Authors: Nakamasa Inoue, Koichi Shinoda
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Assigning a visual code to a low-level image descriptor, which we call code assignment, is the most computationally expensive part of image classification algorithms based on the bag of visual word (BoW) framework. This paper proposes a fast computation method, Neighbor-toNeighbor (NTN) search, for this code assignment. Based on the fact that image features from an adjacent region are usually similar to each other, this algorithm effectively reduces the cost of calculating the distance between a codeword and a feature vector. This method can be applied not only to a hard codebook constructed by vector quantization (NTN-VQ), but also to a soft codebook, a Gaussian mixture model (NTN-GMM). We evaluated this method on the PASCAL VOC 2007 classification challenge task. NTN-VQ reduced the assignment cost by 77.4% in super-vector coding, and NTN-GMM reduced it by 89.3% in Fisher-vector coding, without any significant degradation in classification performance.
Flattening Supervoxel Hierarchies by the Uniform Entropy Slice
Authors: Chenliang Xu, Spencer Whitt, Jason J. Corso
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Supervoxel hierarchies provide a rich multiscale decomposition of a given video suitable for subsequent processing in video analysis. The hierarchies are typically computed by an unsupervised process that is susceptible to undersegmentation at coarse levels and over-segmentation at fine levels, which make it a challenge to adopt the hierarchies for later use. In this paper, we propose the first method to overcome this limitation and flatten the hierarchy into a single segmentation. Our method, called the uniform entropy slice, seeks a selection of supervoxels that balances the relative level of information in the selected supervoxels based on some post hoc feature criterion such as objectness. For example, with this criterion, in regions nearby objects, our method prefers finer supervoxels to capture the local details, but in regions away from any objects we prefer coarser supervoxels. We formulate the uniform entropy slice as a binary quadratic program and implement four different feature criteria, both unsupervised and supervised, to drive the flattening. Although we apply it only to supervoxel hierarchies in this paper, our method is generally applicable to segmentation tree hierarchies. Our experiments demonstrate both strong qualitative performance and superior quantitative performance to state of the art baselines on benchmark internet videos.
Find the Best Path: An Efficient and Accurate Classifier for Image Hierarchies
Authors: Min Sun, Wan Huang, Silvio Savarese
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Many methods have been proposed to solve the image classification problem for a large number of categories. Among them, methods based on tree-based representations achieve good trade-off between accuracy and test time efficiency. While focusing on learning a tree-shaped hierarchy and the corresponding set of classifiers, most of them [11, 2, 14] use a greedy prediction algorithm for test time efficiency. We argue that the dramatic decrease in accuracy at high efficiency is caused by the specific design choice of the learning and greedy prediction algorithms. In this work, we propose a classifier which achieves a better trade-off between efficiency and accuracy with a given tree-shaped hierarchy. First, we convert the classification problem as finding the best path in the hierarchy, and a novel branchand-bound-like algorithm is introduced to efficiently search for the best path. Second, we jointly train the classifiers using a novel Structured SVM (SSVM) formulation with additional bound constraints. As a result, our method achieves a significant 4.65%, 5.43%, and 4.07% (relative 24.82%, 41.64%, and 109.79%) improvement in accuracy at high efficiency compared to state-of-the-art greedy "tree-based" methods [14] on Caltech-256 [15], SUN [32] and ImageNet 1K [9] dataset, respectively. Finally, we show that our branch-and-bound-like algorithm naturally ranks the paths in the hierarchy (Fig. 8) so that users can further process them.
Model Recommendation with Virtual Probes for Egocentric Hand Detection
Authors: Cheng Li, Kris M. Kitani
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills, recognizing gestures and learning about hand-object manipulation. To enable such technology, we believe that the hands must detected on the pixellevel to gain important information about the shape of the hands and fingers. We show that the problem of pixel-wise hand detection can be effectively solved, by posing the problem as a model recommendation task. As such, the goal of a recommendation system is to recommend the n-best hand detectors based on the probe set a small amount of labeled data from the test distribution. This requirement of a probe set is a serious limitation in many applications, such as ego-centric hand detection, where the test distribution may be continually changing. To address this limitation, we propose the use of virtual probes which can be automatically extracted from the test distribution. The key idea is that many features, such as the color distribution or relative performance between two detectors, can be used as a proxy to the probe set. In our experiments we show that the recommendation paradigm is well-equipped to handle complex changes in the appearance of the hands in firstperson vision. In particular, we show how our system is able to generalize to new scenarios by testing our model across multiple users.
Video Motion for Every Visible Point
Authors: Susanna Ricco, Carlo Tomasi
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Dense motion of image points over many video frames can provide important information about the world. However, occlusions and drift make it impossible to compute long motion paths by merely concatenating optical flow vectors between consecutive frames. Instead, we solve for entire paths directly, and flag the frames in which each is visible. As in previous work, we anchor each path to a unique pixel which guarantees an even spatial distribution of paths. Unlike earlier methods, we allow paths to be anchored in any frame. By explicitly requiring that at least one visible path passes within a small neighborhood of every pixel, we guarantee complete coverage of all visible points in all frames. We achieve state-of-the-art results on real sequences including both rigid and non-rigid motions with significant occlusions.
Camera Alignment Using Trajectory Intersections in Unsynchronized Videos
Authors: Thomas Kuo, Santhoshkumar Sunderrajan, B.S. Manjunath
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper addresses the novel and challenging problem of aligning camera views that are unsynchronized by low and/or variable frame rates using object trajectories. Unlike existing trajectory-based alignment methods, our method does not require frame-to-frame synchronization. Instead, we propose using the intersections of corresponding object trajectories to match views. To find these intersections, we introduce a novel trajectory matching algorithm based on matching Spatio-Temporal Context Graphs (STCGs). These graphs represent the distances between trajectories in time and space within a view, and are matched to an STCG from another view to find the corresponding trajectories. To the best of our knowledge, this is one of the first attempts to align views that are unsynchronized with variable frame rates. The results on simulated and real-world datasets show trajectory intersections are a viable feature for camera alignment, and that the trajectory matching method performs well in real-world scenarios.
A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis
Authors: Fabio Galasso, Naveen Shankar Nagaraja, Tatiana Jimenez Cardenas, Thomas Brox, Bernt Schiele
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Video segmentation research is currently limited by the lack of a benchmark dataset that covers the large variety of subproblems appearing in video segmentation and that is large enough to avoid overfitting. Consequently, there is little analysis of video segmentation which generalizes across subtasks, and it is not yet clear which and how video segmentation should leverage the information from the still-frames, as previously studied in image segmentation, alongside video specific information, such as temporal volume, motion and occlusion. In this work we provide such an analysis based on annotations of a large video dataset, where each video is manually segmented by multiple persons. Moreover, we introduce a new volume-based metric that includes the important aspect of temporal consistency, that can deal with segmentation hierarchies, and that reflects the tradeoff between over-segmentation and segmentation accuracy.
Optical Flow via Locally Adaptive Fusion of Complementary Data Costs
Authors: Tae Hyun Kim, Hee Seok Lee, Kyoung Mu Lee
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. In this paper, in contrast to the conventional optical flow framework that uses a single or fixed data model, we study a novel framework that employs locally varying data term that adaptively combines different multiple types of data models. The locally adaptive data term greatly reduces the matching ambiguity due to the complementary nature of the multiple data models. The optimal number of complementary data models is learnt by minimizing the redundancy among them under the minimum description length constraint (MDL). From these chosen data models, a new optical flow estimation energy model is designed with the weighted sum of the multiple data models, and a convex optimization-based highly effective and practical solution that finds the optical flow, as well as the weights is proposed. Comparative experimental results on the Middlebury optical flow benchmark show that the proposed method using the complementary data models outperforms the state-ofthe art methods.
Shortest Paths with Curvature and Torsion
Authors: Petter Strandmark, Johannes Ulen, Fredrik Kahl, Leo Grady
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper describes a method of finding thin, elongated structures in images and volumes. We use shortest paths to minimize very general functionals of higher-order curve properties, such as curvature and torsion. Our globally optimal method uses line graphs and its runtime is polynomial in the size of the discretization, often in the order of seconds on a single computer. To our knowledge, we are the first to perform experiments in three dimensions with curvature and torsion regularization. The largest graphs we process have almost one hundred billion arcs. Experiments on medical images and in multi-view reconstruction show the significance and practical usefulness of regularization based on curvature while torsion is still only tractable for small-scale problems.
Multi-view 3D Reconstruction from Uncalibrated Radially-Symmetric Cameras
Authors: Jae-Hak Kim, Yuchao Dai, Hongdong Li, Xin Du, Jonghyuk Kim
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a new multi-view 3D Euclidean reconstruction method for arbitrary uncalibrated radially-symmetric cameras, which needs no calibration or any camera model parameters other than radial symmetry. It is built on the radial 1D camera model [25], a unified mathematical abstraction to different types of radially-symmetric cameras. We formulate the problem of multi-view reconstruction for radial 1D cameras as a matrix rank minimization problem. Efficient implementation based on alternating direction continuation is proposed to handle scalability issue for real-world applications. Our method applies to a wide range of omnidirectional cameras including both dioptric and catadioptric (central and non-central) cameras. Additionally, our method deals with complete and incomplete measurements under a unified framework elegantly. Experiments on both synthetic and real images from various types of cameras validate the superior performance of our new method, in terms of numerical accuracy and robustness.
Illuminant Chromaticity from Image Sequences
Authors: Veronique Prinet, Dani Lischinski, Michael Werman
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We estimate illuminant chromaticity from temporal sequences, for scenes illuminated by either one or two dominant illuminants. While there are many methods for illuminant estimation from a single image, few works so far have focused on videos, and even fewer on multiple light sources. Our aim is to leverage information provided by the temporal acquisition, where either the objects or the camera or the light source are/is in motion in order to estimate illuminant color without the need for user interaction or using strong assumptions and heuristics. We introduce a simple physically-based formulation based on the assumption that the incident light chromaticity is constant over a short space-time domain. We show that a deterministic approach is not sufficient for accurate and robust estimation: however, a probabilistic formulation makes it possible to implicitly integrate away hidden factors that have been ignored by the physical model. Experimental results are reported on a dataset of natural video sequences and on the GrayBall benchmark, indicating that we compare favorably with the state-of-the-art.
Allocentric Pose Estimation
Authors: M. Jose Antonio, Luc De Raedt, Tinne Tuytelaars
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The task of object pose estimation has been a challenge since the early days of computer vision. To estimate the pose (or viewpoint) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, external elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been shown to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for pose estimation. In particular, we look at object configurations. We show that, starting from noisy object detections and pose estimates, exploiting the estimated pose and location of other objects in the scene can help to estimate the objects' poses more accurately. We explore both a camera-centered as well as an object-centered representation for relations. Experiments on the challenging KITTI dataset show that object configurations can indeed be used as a complementary cue to appearance-based pose estimation. In addition, object-centered relational representations can also assist object detection.
The Interestingness of Images
Authors: Michael Gygli, Helmut Grabner, Hayko Riemenschneider, Fabian Nater, Luc Van Gool
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We investigate human interest in photos. Based on our own and others' psychological experiments, we identify various cues for "interestingness", namely aesthetics, unusualness and general preferences. For the ranking of retrieved images, interestingness is more appropriate than cues proposed earlier. Interestingness is, for example, correlated with what people believe they will remember. This is opposed to actual memorability, which is uncorrelated to both of them. We introduce a set of features computationally capturing the three main aspects of visual interestingness that we propose and build an interestingness predictor from them. Its performance is shown on three datasets with varying context, reflecting diverse levels of prior knowledge of the viewers.
Learning Maximum Margin Temporal Warping for Action Recognition
Authors: Jiang Wang, Ying Wu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Temporal misalignment and duration variation in video actions largely influence the performance of action recognition, but it is very difficult to specify effective temporal alignment on action sequences. To address this challenge, this paper proposes a novel discriminative learning-based temporal alignment method, called maximum margin temporal warping (MMTW), to align two action sequences and measure their matching score. Based on the latent structure SVM formulation, the proposed MMTW method is able to learn a phantom action template to represent an action class for maximum discrimination against other classes. The recognition of this action class is based on the associated learned alignment of the input action. Extensive experiments on five benchmark datasets have demonstrated that this MMTW model is able to significantly promote the accuracy and robustness of action recognition under temporal misalignment and variations.
Rolling Shutter Stereo
Authors: Olivier Saurer, Kevin Koser, Jean-Yves Bouguet, Marc Pollefeys
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.
Fast Sparsity-Based Orthogonal Dictionary Learning for Image Restoration
Authors: Chenglong Bao, Jian-Feng Cai, Hui Ji
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In recent years, how to learn a dictionary from input images for sparse modelling has been one very active topic in image processing and recognition. Most existing dictionary learning methods consider an over-complete dictionary, e.g. the K-SVD method. Often they require solving some minimization problem that is very challenging in terms of computational feasibility and efficiency. However, if the correlations among dictionary atoms are not well constrained, the redundancy of the dictionary does not necessarily improve the performance of sparse coding. This paper proposed a fast orthogonal dictionary learning method for sparse image representation. With comparable performance on several image restoration tasks, the proposed method is much more computationally efficient than the over-complete dictionary based learning methods.
Slice Sampling Particle Belief Propagation
Authors: Oliver Muller, Michael Ying Yang, Bodo Rosenhahn
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation ( PBP ) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings ( MH ) Markov chain Monte Carlo ( MCMC ) methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.
Training Deformable Part Models with Decorrelated Features
Authors: Ross Girshick, Jitendra Malik
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we show how to train a deformable part model (DPM) fast--typically in less than 20 minutes, or four times faster than the current fastest method--while maintaining high average precision on the PASCAL VOC datasets. At the core of our approach is "latent LDA," a novel generalization of linear discriminant analysis for learning latent variable models. Unlike latent SVM, latent LDA uses efficient closed-form updates and does not require an expensive search for hard negative examples. Our approach also acts as a springboard for a detailed experimental study of DPM training. We isolate and quantify the impact of key training factors for the first time (e.g., How important are discriminative SVM filters? How important is joint parameter estimation? How many negative images are needed for training?). Our findings yield useful insights for researchers working with Markov random fields and partbased models, and have practical implications for speeding up tasks such as model selection.
Efficient Salient Region Detection with Soft Image Abstraction
Authors: Ming-Ming Cheng, Jonathan Warrell, Wen-Yan Lin, Shuai Zheng, Vibhav Vineet, Nigel Crook
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elements for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial distribution of image pixels, the proposed representation abstracts out unnecessary image details, allowing the assignment of comparable saliency values across similar regions, and producing perceptually accurate salient region detection. We evaluate our salient region detection approach on the largest publicly available dataset with pixel accurate annotations. The experimental results show that the proposed method outperforms 18 alternate methods, reducing the mean absolute error by 25.2% compared to the previous best result, while being computationally more efficient.
Video Segmentation by Tracking Many Figure-Ground Segments
Authors: Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, James M. Rehg
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose an unsupervised video segmentation approach by simultaneously tracking multiple holistic figureground segments. Segment tracks are initialized from a pool of segment proposals generated from a figure-ground segmentation algorithm. Then, online non-local appearance models are trained incrementally for each track using a multi-output regularized least squares formulation. By using the same set of training examples for all segment tracks, a computational trick allows us to track hundreds of segment tracks efficiently, as well as perform optimal online updates in closed-form. Besides, a new composite statistical inference approach is proposed for refining the obtained segment tracks, which breaks down the initial segment proposals and recombines for better ones by utilizing highorder statistic estimates from the appearance model and enforcing temporal consistency. For evaluating the algorithm, a dataset, SegTrack v2, is collected with about 1,000 frames with pixel-level annotations. The proposed framework outperforms state-of-the-art approaches in the dataset, showing its efficiency and robustness to challenges in different video sequences.
Bayesian 3D Tracking from Monocular Video
Authors: Ernesto Brau, Jinyan Guan, Kyle Simek, Luca Del Pero, Colin Reimer Dawson, Kobus Barnard
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We develop a Bayesian modeling approach for tracking people in 3D from monocular video with unknown cameras. Modeling in 3D provides natural explanations for occlusions and smoothness discontinuities that result from projection, and allows priors on velocity and smoothness to be grounded in physical quantities: meters and seconds vs. pixels and frames. We pose the problem in the context of data association, in which observations are assigned to tracks. A correct application of Bayesian inference to multitarget tracking must address the fact that the model's dimension changes as tracks are added or removed, and thus, posterior densities of different hypotheses are not comparable. We address this by marginalizing out the trajectory parameters so the resulting posterior over data associations has constant dimension. This is made tractable by using (a) Gaussian process priors for smooth trajectories and (b) approximately Gaussian likelihood functions. Our approach provides a principled method for incorporating multiple sources of evidence; we present results using both optical flow and object detector outputs. Results are comparable to recent work on 3D tracking and, unlike others, our method requires no pre-calibrated cameras.
Concurrent Action Detection with Structural Prediction
Authors: Ping Wei, Nanning Zheng, Yibiao Zhao, Song-Chun Zhu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. However, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other. This paper proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions. We use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.
Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach
Authors: Reyes Rios-Cabrera, Tinne Tuytelaars
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.
The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection
Authors: Mihai Zanfir, Marius Leordeanu, Cristian Sminchisescu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Human action recognition under low observational latency is receiving a growing interest in computer vision due to rapidly developing technologies in human-robot interaction, computer gaming and surveillance. In this paper we propose a fast, simple, yet powerful non-parametric Moving Pose (MP) framework for low-latency human action and activity recognition. Central to our methodology is a moving pose descriptor that considers both pose information as well as differential quantities (speed and acceleration) of the human body joints within a short time window around the current frame. The proposed descriptor is used in conjunction with a modified kNN classifier that considers both the temporal location of a particular frame within the action sequence as well as the discrimination power of its moving pose descriptor compared to other frames in the training set. The resulting method is non-parametric and enables low-latency recognition, one-shot learning, and action detection in difficult unsegmented sequences. Moreover, the framework is real-time, scalable, and outperforms more sophisticated approaches on challenging benchmarks like MSR-Action3D or MSR-DailyActivities3D.
Learning a Dictionary of Shape Epitomes with Applications to Image Labeling
Authors: Liang-Chieh Chen, George Papandreou, Alan L. Yuille
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes. These shape epitomes represent the local edge structure of the image and include hidden variables to encode shift and rotations. They are learnt in an unsupervised manner from groundtruth edges. This dictionary is compact but is also able to capture the typical shapes of edges in natural images. In this paper, we illustrate the shape epitomes by applying them to the image labeling task. In other work, described in the supplementary material, we apply them to edge detection and image modeling. We apply shape epitomes to image labeling by using Conditional Random Field (CRF) Models. They are alternatives to the superpixel or pixel representations used in most CRFs. In our approach, the shape of an image patch is encoded by a shape epitome from the dictionary. Unlike the superpixel representation, our method avoids making early decisions which cannot be reversed. Our resulting hierarchical CRFs efficiently capture both local and global class co-occurrence properties. We demonstrate its quantitative and qualitative properties of our approach with image labeling experiments on two standard datasets: MSRC-21 and Stanford Background.
Online Motion Segmentation Using Dynamic Label Propagation
Authors: Ali Elqursh, Ahmed Elgammal
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The vast majority of work on motion segmentation adopts the affine camera model due to its simplicity. Under the affine model, the motion segmentation problem becomes that of subspace separation. Due to this assumption, such methods are mainly offline and exhibit poor performance when the assumption is not satisfied. This is made evident in state-of-the-art methods that relax this assumption by using piecewise affine spaces and spectral clustering techniques to achieve better results. In this paper, we formulate the problem of motion segmentation as that of manifold separation. We then show how label propagation can be used in an online framework to achieve manifold separation. The performance of our framework is evaluated on a benchmark dataset and achieves competitive performance while being online.
Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling
Authors: Evgeny Levinkov, Mario Fritz
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Semantic road labeling is a key component of systems that aim at assisted or even autonomous driving. Considering that such systems continuously operate in the realworld, unforeseen conditions not represented in any conceivable training procedure are likely to occur on a regular basis. In order to equip systems with the ability to cope with such situations, we would like to enable adaptation to such new situations and conditions at runtime. Existing adaptive methods for image labeling either require labeled data from the new condition or even operate globally on a complete test set. None of this is a desirable mode of operation for a system as described above where new images arrive sequentially and conditions may vary. We study the effect of changing test conditions on scene labeling methods based on a new diverse street scene dataset. We propose a novel approach that can operate in such conditions and is based on a sequential Bayesian model update in order to robustly integrate the arriving images into the adapting procedure.
Directed Acyclic Graph Kernels for Action Recognition
Authors: Ling Wang, Hichem Sahbi
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: One of the trends of action recognition consists in extracting and comparing mid-level features which encode visual and motion aspects of objects into scenes. However, when scenes contain high-level semantic actions with many interacting parts, these mid-level features are not sufficient to capture high level structures as well as high order causal relationships between moving objects resulting into a clear drop in performances. In this paper, we address this issue and we propose an alternative action recognition method based on a novel graph kernel. In the main contributions of this work, we first describe actions in videos using directed acyclic graphs (DAGs), that naturally encode pairwise interactions between moving object parts, and then we compare these DAGs by analyzing the spectrum of their sub-patterns that capture complex higher order interactions. This extraction and comparison process is computationally tractable, resulting from the acyclic property of DAGs, and it also defines a positive semi-definite kernel. When plugging the latter into support vector machines, we obtain an action recognition algorithm that overtakes related work, including graph-based methods, on a standard evaluation dataset.
Strong Appearance and Expressive Spatial Models for Human Pose Estimation
Authors: Leonid Pishchulin, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Typical approaches to articulated pose estimation combine spatial modelling of the human body with appearance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representations aiming to substantially improve the body part hypotheses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial models as well as image-conditioned spatial models. In a series of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary; (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-ofthe-art performance when augmented with the proper appearance representation; and (3) we show that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the "Leeds Sports Poses" and "Parse" benchmarks.