nils-herrmann/clean_scirepeval huggingface dataset:
Microchoice bounds and self bounding learning algorithms

A major topic in machine learning is to determine good upper bounds on the true error rates of learned hypotheses based upon their empirical performance on training data. In this paper, we demonstrate new adaptive bounds designed for learning algorithms that operate by making a sequence of choices. These bounds, which we call Microchoice bounds, are similar to Occam-style bounds and can be used to make learning algorithms self-bounding in the style of Freund (1998). We then show how to combine these bounds with Freund's query-tree approach producing a version of Freund's query-tree structure that can be implemented with much more algorithmic efficiency.
Beating the hold-out: bounds for K-fold and progressive cross-validation

The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estimate. K-fold cross validation is used in practice with the hope of being more accurate than the hold-out estimate without reducing the number of training examples. We argue that the k-fold estimate does in fact achieve this goal. Specifically, we show that for any nontrivial learning problem and learning algorithm that is insensitive to example ordering, the k-fold estimate is strictly more accurate than a single hold-out estimate on 1/k of the data, for 2 < k < n (k = n is leave-one-out), based on its variance and all higher moments. Previous bounds were termed sanitycheck because they compared the k-fold estimate to the training estimate and, further, restricted the VC dimension and required a notion of hypothesis stability . In order to avoid these dependencies, we consider a k-fold hypothesis that is a randomized combination or average of the k individual hypotheses. We introduce progressive validationas another possible improvement on the hold-out estimate. This estimate of the generalization error is, in many ways, as good as that of a single hold-out, but it uses an average of half as many examples for testing. The procedure also involves a hold-out set, but after an example has been tested, it is added to the training set and the learning algorithm is rerun.
On learning monotone Boolean functions

We consider the problem of learning monotone Boolean functions over {0, 1}/sup n/ under the uniform distribution. Specifically, given a polynomial number of uniform random samples for an unknown monotone Boolean function f, and given polynomial completing time, we would like to approximate f as well as possible. We describe a simple algorithm that we prove achieves error at most 1/2-/spl Omega/(1//spl radic/n), improving on the previous best bound of 1/2-/spl Omega/((log/sup 2/ n)/n). We also prove that no algorithm, given a polynomial number of samples, can guarantee error 1/2-/spl omega/((log n)//spl radic/n), improving on the previous best hardness bound of O(1//spl radic/n). These lower bounds hold even if the learning algorithm is allowed membership queries. Thus this paper settles to an O(log n) factor the question of the best achievable error for learning the class of monotone Boolean functions with respect to the uniform distribution.
A Variational Approach to Problems in Calibration of Multiple Cameras

This paper addresses the problem of calibrating camera parameters using variational methods. One problem addressed is the severe lens distortion in low-cost cameras. For many computer vision algorithms aiming at reconstructing reliable representations of 3D scenes, the camera distortion effects will lead to inaccurate 3D reconstructions and geometrical measurements if not accounted for. A second problem is the color calibration problem caused by variations in camera responses that result in different color measurements and affects the algorithms that depend on these measurements. We also address the extrinsic camera calibration that estimates relative poses and orientations of multiple cameras in the system and the intrinsic camera calibration that estimates focal lengths and the skew parameters of the cameras. To address these calibration problems, we present multiview stereo techniques based on variational methods that utilize partial and ordinary differential equations. Our approach can also be considered as a coordinated refinement of camera calibration parameters. To reduce computational complexity of such algorithms, we utilize prior knowledge on the calibration object, making a piecewise smooth surface assumption, and evolve the pose, orientation, and scale parameters of such a 3D model object without requiring a 2D feature extraction from camera views. We derive the evolution equations for the distortion coefficients, the color calibration parameters, the extrinsic and intrinsic parameters of the cameras, and present experimental results.
One-Shot Integral Invariant Shape Priors for Variational Segmentation

We match shapes, even under severe deformations, via a smooth re-parametrization of their integral invariant signatures. These robust signatures and correspondences are the foundation of a shape energy functional for variational image segmentation. Integral invariant shape templates do not require registration and allow for significant deformations of the contour, such as the articulation of the object's parts. This enables generalization to multiple instances of a shape from a single template, instead of requiring several templates for searching or training. This paper motivates and presents the energy functional, derives the gradient descent direction to optimize the functional, and demonstrates the method, coupled with a data term, on real image data where the object's parts are articulated.
Using Bilinear Models for View-invariant Identity Recognition from Gait

Human identification from gait is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are shot by a single camera. In this paper we address the problem of finding an effective view-invariant approach for the human ID from gait problem. Given a dataset of sequences in which different people walking normally are seen from several well-separated views, we learn bilinear models to classify the identity of a known person from a view not included in the available training set. Assuming fixed dynamics, each sequence can be mapped to an observation vector with a constant number of poses using a Markov model. We test our approach on the CMU Mobo database, showing how even adopting a rather compact representation for images, bilinear separation of ID and view outperforms the standard baseline algorithm.
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation

We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatio-temporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length.Exploiting the Bayesian framework, we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions. The resulting functional can be interpreted as an extension of the Mumford-Shah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set.We propose two different representations of this motion boundary: an explicit spline-based implementation which can be applied to the motion-based tracking of a single moving object, and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects.Numerical results both for simulated ground truth experiments and for real-world sequences demonstrate the capacity of our approach to segment objects based exclusively on their relative motion.
Dynamic textures: modeling, learning, synthesis, animation, segmentation, and recognition

Dynamic textures are sequences of images of dynamic scenes that exhibit some temporal regularity properties, intended in a statistical sense; these include, for example, ocean waves, smoke, whirlwind, fire, foliage, but also moving objects with a “defined shape,” for instance flowers, or flags in wind etc. This work presents a characterization of this class of video sequences, and poses the problems of modeling, learning, synthesis, animation, recognition, and segmentation of dynamic textures. 
Since, in absence of any additional prior knowledge, the visual reconstruction problem from images alone is ill-posed, in this work we give up trying to infer the physical model that generated the images, and analyze sequences of images solely as visual signals. We do so by building a statistical framework, and draw on disciplines like time series analysis, system, control, and identification theory. 
We derive three generative models, the simplest possible, that are able to capture, respectively, the temporal second-order statistics, the spatio-temporal second-order statistics, and the higher-order temporal statistics of dynamic textures. We propose to learn model parameters in the maximum-likelihood sense, or minimum prediction error variance. We derive efficient closed-form inference procedures for learning the second-order statistics, and revert to non-linear optimization techniques for the higher-order ones. After learning a model, it can be used to extrapolate, or predict new image data both in the temporal and spatial domain. We analyze the meaning of the parameters of a model, and show how they can be manipulated to control, or animate the simulation. Using the geometry of subspaces, and statistical pattern recognition theory we derive a technique to discriminate between models, and assess the potential for building a recognition system. Finally, by combining these results with a variational framework, we design a region-based segmentation system able to partition a video sequence into regions characterized by different spatio-temporal statistics.
On Viewpoint Invariance for Non-Planar Scenes

Current local feature detectors/descriptors implicitly assume that the scene is (locally) planar, an assumption that is violated at surface discontinuities. We show that this restriction is, at least in theory, un-necessary, as one can construct local features that are viewpoint-invariant for generic non-planar scenes. However, we show that any such feature necessarily sacrifices shape information, in the sense of being non shape-discriminative. Finally, we show that if viewpoint is factored out as part of the matching process, rather than explicitly in the representation, then shape is discriminative indeed. We illustrate our theoretical results empirically by showing that, even for simplistic scenes, current affine descriptors fail where even a naive 3-D viewpoint invariant succeeds in matching.
Multi-View Stereo Reconstruction of Dense Shape and Complex Appearance

We address the problem of estimating the three-dimensional shape and complex appearance of a scene from a calibrated set of views under fixed illumination. Our approach relies on a rank condition that must be satisfied when the scene exhibits “specular + diffuse” reflectance characteristics. This constraint is used to define a cost functional for the discrepancy between the measured images and those generated by the estimate of the scene, rather than attempting to match image-to-image directly. Minimizing such a functional yields the optimal estimate of the shape of the scene, represented by a dense surface, as well as its radiance, represented by four functions defined on such a surface. These can be used to generate novel views that capture the non-Lambertian appearance of the scene.
Learning Riemannian Metrics for Classification of Dynamical Models

Consider the problem of classifying motions, encoded as dynamical models of a certain class. Standard nearest neighbor classification then reduces to find a suitable distance function in the space of the models. In this paper we present a supervised differential-geometric method to learn a Riemannian metric for a given class of dynamical models in order to improve classification performances. Given a training set of models the optimal metric is selected among a family of pullback metrics induced by the Fisher information tensor through a parameterized diffeomorphism. Experimental results concerning action and identity recognition based on simple scalar features are shown, proving how learning a metric actually improves classification rates when compared with Fisher geodesic distance and other classical distance functions.
Features for recognition: viewpoint invariance for non-planar scenes

Most current local feature detectors/descriptors implicitly assume that the scene is (locally) planar, an assumption that is violated at surface discontinuities. We show that this restriction is, at least in theory, unnecessary, as one can construct local features that are viewpoint-invariant for generic non-planar scenes. However, we show that any such feature necessarily sacrifices shape information, in the sense of being non shape-discriminative. Finally, we show that if viewpoint is factored out as part of the matching process, rather than explicitly in the representation, then shape is discriminative indeed. We illustrate our theoretical results empirically by showing that, even for simple scenes, current affine descriptors fail where even a naive 3-D viewpoint invariant succeeds in matching
KALMANSAC: robust filtering by consensus

We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering algorithms in a sampling framework.
Classifying Human Dynamics Without Contact Forces UCLA CSD-TR 050045

We develop a classification algorithm for hybrid autoregressive models of human motion for the purpose of video-based analysis and recognition. We assume that some temporal statistics are extracted from the images, and we use them to infer a dynamical system that explicitly models contact forces. We then develop a distance between such models that explicitly factors out exogenous inputs that are not unique to an individual or her gait. We show that such a distance is far more discriminative than the distance between simple linear systems, where most of the energy is devoted to modeling the dynamics of spurious nuisances such as contact forces.
KALMANSAC : Causal Inference of Dynamic Processes Drowned in Outliers With Application to Tracking and Real-time Structure From Motion

We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering algorithms in a sampling framework.
Fast Marching Method for Generic Shape from Shading

We develop a fast numerical method to approximate the solutions of a wide class of equations associated to the Shape From Shading problem. Our method, which is based on the control theory and the interfaces propagation, is an extension of the “Fast Marching Method” (FMM) [30,25]. In particular our method extends the FMM to some equations for which the solution is not systematically decreasing along the optimal trajectories. We apply with success our one-pass method to the Shape From Shading equations which are involved by the most relevant and recent modelings [22,21] and which cannot be handled by the most recent extensions of the FMM [26,8].
Conference on Computer Vision and Pattern Recognition

Welcome to San Diego, California and the 2005  Conference on Computer Vision and Pattern Recognition (CVPR), held on June 20-26. This year's conference is the twenty-third in the CVPR series. Besides the main conference, CVPR includes a series of workshops and tutorials, demonstrations and exhibits, and a video proceedings. Conforming with recent CVPR's, registrants for the main conference have a passport to attend any of the other events, and all of the proceedings are presented on a single DVD. We hope that you'll explore all that CVPR has to offer.
A geometric approach to shape from defocus

We introduce a novel approach to shape from defocus, i.e., the problem of inferring the three-dimensional (3D) geometry of a scene from a collection of defocused images. Typically, in shape from defocus, the task of extracting geometry also requires deblurring the given images. A common approach to bypass this task relies on approximating the scene locally by a plane parallel to the image (the so-called equifocal assumption). We show that this approximation is indeed not necessary, as one can estimate 3D geometry while avoiding deblurring without strong assumptions on the scene. Solving the problem of shape from defocus requires modeling how light interacts with the optics before reaching the imaging surface. This interaction is described by the so-called point spread function (PSF). When the form of the PSF is known, we propose an optimal method to infer 3D geometry from defocused images that involves computing orthogonal operators which are regularized via functional singular value decomposition. When the form of the PSF is unknown, we propose a simple and efficient method that first learns a set of projection operators from blurred images and then uses these operators to estimate the 3D geometry of the scene from novel blurred images. Our experiments on both real and synthetic images show that the performance of the algorithm is relatively insensitive to the form of the PSF Our general approach is to minimize the Euclidean norm of the difference between the estimated images and the observed images. The method is geometric in that we reduce the minimization to performing projections onto linear subspaces, by using inner product structures on both infinite and finite-dimensional Hilbert spaces. Both proposed algorithms involve only simple matrix-vector multiplications which can be implemented in real-time.
Dynamical Models for Human Gait Synthesis

We present a novel approach to modeling human gaits such as walking and running. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system that is made up of two subsystems, an autonomous subsystem and a subsystem driven by an unknown stochastic input. We present techniques for inferring model parameters and input signal distributions from data. In particular, we exploit the higher-order statistical information content in motion capture data to arrive at input signals with independent components. We show that we can synthesize arbitrarily long sequences of human gaits that capture the dynamic complexities and the distinct character of the original gait sequences.
Dynamic Shape and Appearance Modeling Via Moving and Deforming Layers

We propose a model of the shape, motion and appearance of a sequence of images that captures occlusions, scene deformations, arbitrary viewpoint variations and changes in irradiance. This model is based on a collection of overlapping layers that can move and deform, each supporting an intensity function that can change over time. We discuss the generality and limitations of this model in relations to existing ones such as traditional optical flow or motion segmentation, layers, deformable templates and deformotion. We then illustrate how this model can be used for inference of shape, motion, deformation and appearance of the scene from a collection of images. The layering structure allows for automatic inpainting of partially occluded regions. We illustrate the model on synthetic and real sequences where existing schemes fail; we implement our gradient-based infinite-dimensional optimization using level set methods.
Layered active appearance models

Active appearance models (AAMs) provide a framework for modeling the joint shape and texture of an image. An AAM is a compact representation of both factors in a conditionally linear model. However, the standard AAM framework does not handle images which have missing features, or allow modification of certain structures in the image while leaving neighboring ones undeformed. We introduce the layered active appearance model (LAAM), which allows for missing features, occlusion, substantial spatial rearrangement of features, and which provides a more general representation that extends the applicability of the active appearance model
Geometry and Reconstruction from Symmetry

In Chapter 6 we have illustrated how prior assumptions on the scene can be exploited to simplify, or in some case enable, the reconstruction of camera pose and calibration. For instance, the presence of parallel lines and right angles in the scene allows one to upgrade the projective reconstruction to affine and even Euclidean. In this chapter, we generalize these concepts to the case where the scene contains objects that are symmetric. While we will make this notion precise shortly, the intuitive terms of “regular structures,” (deterministic) “patterns,” “tiles,” etc. can all be understood in terms of symmetries .