nils-herrmann/clean_scirepeval huggingface dataset:
Learned Collaborative Representations for Image Classification

The collaborative representation-based classifier (CRC) is proposed as an alternative to the sparse representation based classifier (SRC) for image face recognition. CRC solves an l2-regularized least squares formulation, with algebraic solution, while SRC optimizes over an I1-regularized least squares problem. As an extension of CRC, the weighted collaborative representation-based classifier (WCRC) is further proposed. The weights in WCRC are picked intuitively, it remains unclear why such choice of weights works and how we optimize those weights. In this paper, we propose a learned collaborative representation based classifier (LCRC) and attempt to answer the above questions. Our learning technique is based on the fixed point theorem and we use a weights formulation similar to WCRC as the starting point. Through extensive experiments on face datasets we show that the learning procedure is stable and convergent, and that LCRC is able to improve in performance over CRC and WCRC, while keeping the same computational efficiency at test.
Human Pose Estimation Using Body Parts Dependent Joint Regressors

In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.
Viewpoint consistent texture synthesis

The purpose of this work is to synthesize textures of rough, real world surfaces under freely chosen viewing and illumination directions. Moreover, such textures are produced for continuously changing directions in such a way that the different textures are mutually consistent, i.e. emulate the same piece of surface. This is necessary for 3D animation. It is assumed that the mesostructure (small-scale) geometry of a surface is not known, and that the only input consists of a set of images, taken under different viewing and illumination directions. These are automatically aligned to build an appropriate bidirectional texture function (BTF). Directly extending 2D synthesis methods for pixels to complete BTF columns has drawbacks which are exposed, and a superior sequential but highly parallelizable algorithm is proposed. Examples demonstrate the quality of the results.
Real time 3D face alignment with Random Forests-based Active Appearance Models

Many desirable applications dealing with automatic face analysis rely on robust facial feature localization. While extensive research has been carried out on standard 2D imagery, recent technological advances made the acquisition of 3D data both accurate and affordable, opening new ways to more accurate and robust algorithms. We present a model-based approach to real time face alignment, fitting a 3D model to depth and intensity images of unseen expressive faces. We use random regression forests to drive the fitting in an Active Appearance Model framework. We thoroughly evaluated the proposed approach on publicly available datasets and show how adding the depth channel boosts the robustness and accuracy of the algorithm.
Boosting Object Proposals: From Pascal to COCO

Computer vision in general, and object proposals in particular, are nowadays strongly influenced by the databases on which researchers evaluate the performance of their algorithms. This paper studies the transition from the Pascal Visual Object Challenge dataset, which has been the benchmark of reference for the last years, to the updated, bigger, and more challenging Microsoft Common Objects in Context. We first review and deeply analyze the new challenges, and opportunities, that this database presents. We then survey the current state of the art in object proposals and evaluate it focusing on how it generalizes to the new dataset. In sight of these results, we propose various lines of research to take advantage of the new benchmark and improve the techniques. We explore one of these lines, which leads to an improvement over the state of the art of +5.2%.
Mirror and point symmetry under perspective skewing

Over recent years, symmetry research has shifted from the detection of affinely to perspectively skewed mirror symmetry. Also, links between invariance research and symmetry-specific geometric constraints have been established. The paper aims to contribute to both strands. Several sets of symmetry specific invariants are derived, that can be used in different situations, depending on the a priori assumptions made. It is also argued that all the results directly apply to the case of perspectively skewed point symmetry.
Hierarchical coarse to fine depth estimation for realistic view interpolation

This paper presents a novel approach for view synthesis and image interpolation. The algorithm is build up in a hierarchical way, and this on different structural levels instead of using a classic image pyramid. First coarse matching is done on a 'shape basis' only. A background-foreground segmentation yields a fairly accurate contour for every incoming video stream. Inter-relating these contours is a 1D problem and as such very fast. This step is then used to compute small position dependent bounding-boxes in 3D space which enclose the underlying object. The next step is a more expensive window based matching, within the volume of these bounding-boxes. This is limited to a number of regions around 'promising' feature points. Global regularisation is obtained by a graph cut. Speed results here from limiting the number of feature points. In a third step the interpolation is 'pre-rendered' and simultaneously evaluated on a per pixel basis. This is done by computing a Birchfield dissimilarity measure on the GPU. Per pixel parallelised operations keep computational cost low. Finally the bad interpolated parts are 'patched'. This per pixel correction yields the final interpolated view at the finest level. Here we also deal explicitly with opacity at the borders of the foreground object.
A Generalized EM Approach for 3D Model Based Face Recognition under Occlusions

This paper describes an algorithm for pose and illumination invariant face recognition from a single image under occlusions. The method iteratively estimates the parameters of a 3D morphable face model to approximate the appearance of a face in an image. Simultaneously, a visibility map is computed which segments the image into visible and occluded regions. The visibility map is incorporated into a probabilistic image formation model as a set of spatially correlated random variables. This leads to a Generalized Expectation-Maximization algorithm in which the estimation of the morphable model related parameters is interleaved with visibility computations. The validity of the algorithm is verified by a face recognition experiment using images from the publicly available AR Face Database.
Seven Ways to Improve Example-Based Single Image Super Resolution

In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning. We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial improvements. The techniques are widely applicable and require no changes or only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method sets new stateof-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.
Semantic Instance Segmentation for Autonomous Driving

Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. Our approach of combining an offthe- shelf network with a principled loss function inspired by a metric learning objective is conceptually simple and distinct from recent efforts in instance segmentation and is well-suited for real-time applications. In contrast to previous works, our method does not rely on object proposals or recurrent mechanisms and is particularly well suited for tasks with complex occlusions. A key contribution of our work is to demonstrate that such a simple setup without bells and whistles is effective and can perform on-par with more complex methods. We achieve competitive performance on the Cityscapes segmentation benchmark.
Some Like It Hot — Visual Guidance for Preference Prediction

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer interperson preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individuals movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.
Action snippets: How many frames does human action recognition require?

Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large look-ahead to classify each frame. Contrary to these strategies, human vision proves that simple actions can be recognised almost instantaneously. In this paper, we present a system for action recognition from very short sequences (ldquosnippetsrdquo) of 1-10 frames, and systematically evaluate it on standard data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve ap90% correct recognitions, and snippets of 5-7 frames (0.3-0.5 seconds of video) are enough to achieve a performance similar to the one obtainable with the entire video sequence.
Reconstruction of Inextensible Surfaces on a Budget via Bootstrapping

Many methods for 3D reconstruction of deformable surfaces from a monocular view rely on inextensibility constraints. An interesting application with commercial potential lies in augmented reality in portable and wearable devices. Such applications add an additional challenge to the 3D reconstruction, since in portable platforms the availability of resources is limited and not always guaranteed. Towards this goal, we introduce a method to deliver the best possible 3D reconstruction of the deformable surface at any time. Since computational resources may vary, it is decided on-the-fly when to stop the reconstruction algorithm. We use an efficient optimization method to quickly deliver the reconstructed surface. We introduce bootstrapping to improve the robustness of the efficient 3D reconstruction algorithm by merging multiple versions of the reconstructed surface. Also, these multiple 3D surfaces can be used to estimate the confidence of the reconstruction. In a series of experiments, in both synthetic and real data, we show that our method is effective for timely reconstruction of 3D surfaces.
Fast 3D Scanning with Automatic Motion Compensation

We present a novel 3D scanning system combining stereo and active illumination based on phase-shift for robust and accurate scene reconstruction. Stereo overcomes the traditional phase discontinuity problem and allows for the reconstruction of complex scenes containing multiple objects. Due to the sequential recording of three patterns, motion will introduce artifacts in the reconstruction. We develop a closed-form expression for the motion error in order to apply motion compensation on a pixel level. The resulting scanning system can capture accurate depth maps of complex dynamic scenes at 17 fps and can cope with both rigid and deformable objects.
A graph cut based adaptive structured light approach for real-time range acquisition

This work describes a new algorithm that yields dense range maps in real-time. Reconstructions are based on a single frame structured light illumination. On-the-fly adaptation of the projection pattern renders the system more robust against scene variability. A continuous trade off between speed and quality is made. The correspondence problem is solved by using geometric pattern coding in combination with sparse color coding. Only local spatial and temporal continuity are assumed. This allows to construct a neighbor relationship within every frame and to track correspondences over time. All cues are integrated in one consistent labeling. This is achieved by reformulating the problem as a graph cut. Every cue is weighted based on its average consistency with the result within a small time window. Integration and weighting of additional cues is straightforward. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only. Frame rates vary between 10 and 25 fps dependent on scene complexity.
Hough Forests for Object Detection, Tracking, and Action Recognition

The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark data sets and comparisons with the state-of-the-art.
Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors

This paper introduces a simple yet effective method to improve visual word based image retrieval. Our method is based on an analysis of the k-reciprocal nearest neighbor structure in the image space. At query time the information obtained from this process is used to treat different parts of the ranked retrieval list with different distance measures. This leads effectively to a re-ranking of retrieved images. As we will show, this has two benefits: first, using different similarity measures for different parts of the ranked list allows for compensation of the “curse of dimensionality”. Second, it allows for dealing with the uneven distribution of images in the data space. Dealing with both challenges has very beneficial effect on retrieval accuracy. Furthermore, a major part of the process happens offline, so it does not affect speed at retrieval time. Finally, the method operates on the bag-of-words level only, thus it could be combined with any additional measures on e.g. either descriptor level or feature geometry making room for further improvement. We evaluate our approach on common object retrieval benchmarks and demonstrate a significant improvement over standard bag-of-words retrieval.
Bayesian Grammar Learning for Inverse Procedural Modeling

Within the fields of urban reconstruction and city modeling, shape grammars have emerged as a powerful tool for both synthesizing novel designs and reconstructing buildings. Traditionally, a human expert was required to write grammars for specific building styles, which limited the scope of method applicability. We present an approach to automatically learn two-dimensional attributed stochastic context-free grammars (2D-ASCFGs) from a set of labeled building facades. To this end, we use Bayesian Model Merging, a technique originally developed in the field of natural language processing, which we extend to the domain of two-dimensional languages. Given a set of labeled positive examples, we induce a grammar which can be sampled to create novel instances of the same building style. In addition, we demonstrate that our learned grammar can be used for parsing existing facade imagery. Experiments conducted on the dataset of Haussmannian buildings in Paris show that our parsing with learned grammars not only outperforms bottom-up classifiers but is also on par with approaches that use a manually designed style grammar.
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels

State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces. In turn, our interest lies in an approximate, but light-weight method that is better to consider for large-scale applications, such as urban scene reconstruction from ground-based images. We present a novel approach for producing dense reconstructions from multiple images and from the underlying sparse Structure-from-Motion (SfM) data in an efficient way. To overcome the problem of SfM sparsity and textureless areas, we assume piecewise planarity of man-made scenes and exploit both sparse visibility and a fast over-segmentation of the images. Reconstruction is formulated as an energy-driven, multi-view plane assignment problem, which we solve jointly over superpixels from all views while avoiding expensive photoconsistency computations. The resulting planar primitives -- defined by detailed superpixel boundaries -- are computed in about 10 seconds per image.
Ensemble Projection for Semi-supervised Image Classification

This paper investigates the problem of semi-supervised classification. Unlike previous methods to regularize classifying boundaries with unlabeled data, our method learns a new image representation from all available data (labeled and unlabeled) and performs plain supervised learning with the new feature. In particular, an ensemble of image prototype sets are sampled automatically from the available data, to represent a rich set of visual categories/attributes. Discriminative functions are then learned on these prototype sets, and image are represented by the concatenation of their projected values onto the prototypes (similarities to them) for further classification. Experiments on four standard datasets show three interesting phenomena: (1) our method consistently outperforms previous methods for semi-supervised image classification, (2) our method lets itself combine well with these methods, and (3) our method works well for self-taught image classification where unlabeled data are not coming from the same distribution as labeled ones, but rather from a random collection of images.
Full body tracking from multiple views using stochastic sampling

We present a novel approach for full body pose tracking using stochastic sampling. A volumetric reconstruction of a person is extracted from silhouettes in multiple video images. Then, an articulated body model is fitted to the data with stochastic meta descent (SMD) optimization. By comparing even a simplified version of SMD to the commonly used Levenberg-Marquardt method, we demonstrate the power of stochastic compared to deterministic sampling, especially in cases of noisy and incomplete data. Moreover, color information is added to improve the speed and robustness of the tracking. Results are shown for several challenging sequences, with tracking of 24 degrees of freedom in less than 1 second per frame.
Event-Based Tracking Evaluation Metric

This paper describes a novel tracking performance evaluation metric based on the successful detection of events, rather than low-level image processing criteria. A general event metric is defined to measure whether the agents and actions in the scene given by the ground truth were correctly tracked by comparing two event lists using dynamic programming. This metric is suitable to evaluate and compare different tracking approaches where the underlying algorithm may be completely different. Furthermore, we introduce an automatic extraction of those semantically high level events from different types of low level tracking data and human annotated ground truth. A case study with two different trackers on public datasets shows the effectiveness of this evaluation scheme.
Tracking a hand manipulating an object

We present a method for tracking a hand while it is interacting with an object. This setting is arguably the one where hand-tracking has most practical relevance, but poses significant additional challenges: strong occlusions by the object as well as self-occlusions are the norm, and classical anatomical constraints need to be softened due to the external forces between hand and object. To achieve robustness to partial occlusions, we use an individual local tracker for each segment of the articulated structure. The segments are connected in a pairwise Markov random field, which enforces the anatomical hand structure through soft constraints on the joints between adjacent segments. The most likely hand configuration is found with belief propagation. Both range and color data are used as input. Experiments are presented for synthetic data with ground truth and for real data of people manipulating objects.