nils-herrmann/clean_scirepeval huggingface dataset:
3D hand pose reconstruction using specialized mappings

A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA's fundamental components are a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which in our case are examples of hand joint configurations and their corresponding visual features. The joint angle data in the training set is obtained via a CyberGlove, a glove with 22 sensors that monitor the angular motions of the palm and fingers. In training, the visual features are generated using a computer graphics module that renders the hand from arbitrary viewpoints given the 22 joint angles. The viewpoint is encoded by two real values, therefore 24 real values represent a hand pose. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both bands of the user, calculates the appropriate features, and estimates the 3D hand joint angles and viewpoint from those features. Results are encouraging given the complexity of the task.
Action Recognition and Localization by Hierarchical Space-Time Segments

We propose Hierarchical Space-Time Segments as a new representation for action recognition and localization. This representation has a two-level hierarchy. The first level comprises the root space-time segments that may contain a human body. The second level comprises multi-grained space-time segments that contain parts of the root. We present an unsupervised method to generate this representation from video, which extracts both static and non-static relevant space-time segments, and also preserves their hierarchical and temporal relationships. Using simple linear SVM on the resultant bag of hierarchical space-time segments representation, we attain better than, or comparable to, state-of-the-art action recognition performance on two challenging benchmark datasets and at the same time produce good action localization results.
Non-rigid motion and structure from contour

The elastic properties of real materials constrain the types of non-rigid motion that can occur. The authors show that by modeling and simulating the physics of non-rigid motion they can obtain good estimates of both object shape and velocity from contour data. The underlying model is based on the finite element method, and decouples the degrees of freedom by breaking down object motion into rigid and non-rigid vibration modes. Shape estimates are integrated over time by use of an extended Kalman filter, resulting in a stable estimate of both 3D shape and 3D velocity. Examples using X-ray imagery (of the heat) are presented.<<ETX>>
Active voodoo dolls: a vision based input device for nonrigid control

A vision based technique for nonrigid control is presented that can be used for animation and video game applications. The user grasps a soft, squishable object in front of a camera that can be moved and deformed in order to specify motion. Active Blobs, a nonrigid tracking technique is used to recover the position, rotation and nonrigid deformations of the object. The resulting transformations can be applied to a texture mapped mesh, thus allowing the user to control it interactively. Our use of texture mapping hardware in tracking makes the system responsive enough for interactive animation and video game character control.
Scale and rotation invariant matching using linearly augmented trees

We propose a novel linearly augmented tree method for efficient scale and rotation invariant object matching. The proposed method enforces pairwise matching consistency defined on trees, and high-order constraints on all the sites of a template. The pairwise constraints admit arbitrary metrics while the high-order constraints use L1 norms and therefore can be linearized. Such a linearly augmented tree formulation introduces hyperedges and loops into the basic tree structure, but different from a general loopy graph, its special structure allows us to relax and decompose the optimization into a sequence of tree matching problems efficiently solvable by dynamic programming. The proposed method also works on continuous scale and rotation parameters; we can match with a scale up to any large number with the same efficiency. Our experiments on ground truth data and a variety of real images and videos show that the proposed method is efficient, accurate and reliable.
Online Multi-person Tracking by Tracker Hierarchy

Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers' initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian tracking benchmark datasets, our formulation attains accuracy that is comparable to, or better than, the state-of-the-art pedestrian trackers that must exploit calibration information and operate offline.
Coupling detection and data association for multiple object tracking

We present a novel framework for multiple object tracking in which the problems of object detection and data association are expressed by a single objective function. The framework follows the Lagrange dual decomposition strategy, taking advantage of the often complementary nature of the two subproblems. Our coupling formulation avoids the problem of error propagation from which traditional “detection-tracking approaches” to multiple object tracking suffer. We also eschew common heuristics such as “nonmaximum suppression” of hypotheses by modeling the joint image likelihood as opposed to applying independent likelihood assumptions. Our coupling algorithm is guaranteed to converge and can handle partial or even complete occlusions. Furthermore, our method does not have any severe scalability issues but can process hundreds of frames at the same time. Our experiments involve challenging, notably distinct datasets and demonstrate that our method can achieve results comparable to those of state-of-art approaches, even without a heavily trained object detector.
Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation

Identifying correspondences between trajectory segments observed from nonsynchronized cameras is important for reconstruction of the complete trajectory of moving targets in a large scene. Such a reconstruction can be obtained from motion data by comparing the trajectory segments and estimating both the spatial and temporal alignments. Exhaustive testing of all possible correspondences of trajectories over a temporal window is only viable in the cases with a limited number of moving targets and large view overlaps. Therefore, alternative solutions are required for situations with several trajectories that are only partially visible in each view. In this paper, we propose a new method that is based on view-invariant representation of trajectories, which is used to produce a sparse set of salient points for trajectory segments observed in each view. Only the neighborhoods at these salient points in the view-invariant representation are then used to estimate the spatial and temporal alignment of trajectory pairs in different views. It is demonstrated that, for planar scenes, the method is able to recover with good precision and efficiency both spatial and temporal alignments, even given relatively small overlap between views and arbitrary (unknown) temporal shifts of the cameras. The method also provides the same capabilities in the case of trajectories that are only locally planar, but exhibit some nonplanarity at a global level.
Periodic Motion Detection and Estimation via Space-Time Sampling

A novel technique to detect and localize periodic movements in video is presented. The distinctive feature of the technique is that it requires neither feature tracking nor object segmentation. Intensity patterns along linear sample paths in space-time are used in estimation of period of object motion in a given sequence of frames. Sample paths are obtained by connecting (in space-time) sample points from regions of high motion magnitude in the first and last frames. Oscillations in intensity values are induced at time instants when an object intersects the sample path. The locations of peaks in intensity are determined by parameters of both cyclic object motion and orientation of the sample path with respect to object motion. The information about peaks is used in a least squares framework to obtain an initial estimate of these parameters. The estimate is further refined using the full intensity profile. The best estimate for the period of cyclic object motion is obtained by looking for consensus among estimates from many sample paths. The proposed technique is evaluated with synthetic videos where ground-truth is known, and with American Sign Language videos where the goal is to detect periodic hand motions.
An alignment based similarity measure for hand detection in cluttered sign language video

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a Web cam in a user's home. Moreover, the signers' clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The histogram of oriented gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a support vector machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the vocabulary guided pyramid match kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.
Salient Object Subitizing

People can immediately and precisely identify that an image contains 1, 2, 3 or 4 items by a simple glance. The phenomenon, known as Subitizing, inspires us to pursue the task of Salient Object Subitizing (SOS), i.e. predicting the existence and the number of salient objects in a scene using holistic cues. To study this problem, we propose a new image dataset annotated using an online crowdsourcing marketplace. We show that a proposed subitizing technique using an end-to-end Convolutional Neural Network (CNN) model achieves significantly better than chance performance in matching human labels on our dataset. It attains 94% accuracy in detecting the existence of salient objects, and 42–82% accuracy (chance is 20%) in predicting the number of salient objects (1, 2, 3, and 4+), without resorting to any object localization process. Finally, we demonstrate the usefulness of the proposed subitizing technique in two computer vision applications: salient object detection and object proposal.
Learning and synthesizing human body motion and posture

A novel approach is presented for estimating human body posture and motion from a video sequence. Human pose is defined as the instantaneous image plane configuration of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these visual features to body configurations. Given a set of body motion sequences for training, a set of clusters is built in which each has statistically similar configurations. This unsupervised task is done using the expectation maximization algorithm. Then, for each of the clusters, a neural network is trained to build this mapping. Clustering body configurations improves the mapping accuracy. Given new visual features, a mapping from each cluster is performed providing a set of possible poses. From this set, the most likely pose is extracted given the learned probability distribution and the visual feature similarity between hypothesis and input. Performance of the system is characterized using a new set of known body postures, showing promising results.
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American sign language (ASL).
ClassMap: Efficient Multiclass Recognition via Embeddings

In many computer vision applications, such as face recognition and hand pose estimation, we need systems that can recognize a very large number of classes. Large margin classification methods, such as AdaBoost and SVMs, often provide competitive accuracy rates, but at the cost of evaluating a large number of binary classifiers. We propose an embedding-based method for efficient multiclass recognition. In our method, patterns and classes are mapped to vectors in such a way that patterns and their associated classes tend to get mapped close to each other. This way, given a test pattern, a small set of candidate classes can be identified efficiently using simple vector comparisons. In experiments with 3D hand pose recognition (2430 classes) and face recognition (535 classes), our method is between 3 and 28 times faster compared to evaluating all binary classifiers, with negligible or no loss in classification accuracy.
A modal framework for correspondence and description

The authors describe a framework for establishing correspondence, computing canonical descriptions, and recognizing objects that is based on the idea of describing objects by their generalized symmetries, as defined by the object's free vibration modes. A technique given by A. Pentland and S. Scarloff (1991) described objects in terms of the modes of some prototype shape. In contrast, this new method computes the object's modes directly from available image information. This results in greater generality and accuracy, and is applicable to data of any dimensionality. For the purposes of illustration, a detailed mathematical formulation of the method is given for 2-D problems, and it is demonstrated on gray-scale image and contour data.<<ETX>>
Segmentation by minimal description

The authors formulate the segmentation task as a search for a set of descriptions which minimally encodes a scene. A novel framework for cooperative robust estimation is used to estimate descriptions that locally provide the most savings in encoding an image. A modified Hopfield-Tank networks finds the subset of these descriptions which best describes an entire scene, accounting for occlusion and transparent overlap among individual descriptions. Using a part-based 3-D shape model the authors have implemented a system that is able to successfully segment images into their constituent structure.<<ETX>>
Estimating 3D body pose using uncalibrated cameras

An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a "virtual camera" is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a consequence, the calibration matrices (intrinsics) of the virtual cameras can be considered completely known, and their poses are known up to a single angular displacement parameter Given pose hypotheses obtained in the multiple virtual camera views, the recovery of 3D body pose and camera relative orientations is formulated as a stochastic optimization problem.
Adaptive Hashing for Fast Similarity Search

With the staggering growth in image and video datasets, algorithms that provide fast similarity search and compact storage are crucial. Hashing methods that map the data into Hamming space have shown promise, however, many of these methods employ a batch-learning strategy in which the computational cost and memory requirements may become intractable and infeasible with larger and larger datasets. To overcome these challenges, we propose an online learning algorithm based on stochastic gradient descent in which the hash functions are updated iteratively with streaming data. In experiments with three image retrieval benchmarks, our online algorithm attains retrieval accuracy that is comparable to competing state-of-the-art batch-learning solutions, while our formulation is orders of magnitude faster and being online it is adaptable to the variations of the data. Moreover, our formulation yields improved retrieval performance over a recently reported online hashing technique, Online Kernel Hashing.
Deformable Shape Detection and Description via Model-Based Region Grouping

Certain thermoplastic blends have substantially improved hot strength (i.e., cohesive strength and resistance to sagging or tearing during milling or other hot processing). The blends comprise (1) at least one ethylene-propylene (EP) or ethylene-propylene-diene (EPDM) polymer and (2) at least one highly crystalline polyallomer, and are useful for tubing, wire and cable insulation, molded items and the like.
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical strength across subjects. Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy. We also develop methods for adapting our model to new subjects when a small number of subject-specific personalization data is available. Finally, we investigate active learning algorithms for interactively labeling personalization data in resource-constrained scenarios. Focusing on the problem of gesture recognition where inter-subject variations are commonplace, we demonstrate the effectiveness of our proposed techniques. We test our framework on three widely used gesture recognition datasets, achieving personalization performance competitive with the state-of-the-art.
Learning Activity Progression in LSTMs for Activity Detection and Early Detection

In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks. Conventionally, when training a Recurrent Neural Network, specifically a Long Short Term Memory (LSTM) model, the training loss only considers classification error. However, we argue that the detection score of the correct activity category, or the detection score margin between the correct and incorrect categories, should be monotonically non-decreasing as the model observes more of the activity. We design novel ranking losses that directly penalize the model on violation of such monotonicities, which are used together with classification loss in training of LSTM models. Evaluation on ActivityNet shows significant benefits of the proposed ranking losses in both activity detection and early detection tasks.
Automatic 2D Hand Tracking in Video Sequences

In gesture and sign language video sequences, hand motion tends to be rapid, and hands frequently appear in front of each other or in front of the face. Thus, hand location is often ambiguous, and naive color-based hand tracking is insufficient. To improve tracking accuracy, some methods employ a prediction-update framework, but such methods require careful initialization of model parameters, and tend to drift and lose track in extended sequences. In this paper, a temporal filtering framework for hand tracking is proposed that can initialize and reset itself without human intervention. In each frame, simple features like color and motion residue are exploited to identify multiple candidate hand locations. The temporal filter then uses the Viterbi algorithm to select among the candidates from frame to frame. The resulting tracking system can automatically identify video trajectories of unambiguous hand motion, and detect frames where tracking becomes ambiguous because of occlusions or overlaps. Experiments on video sequences of several hundred frames in duration demonstrate the system's ability to track hands robustly, to detect and handle tracking ambiguities, and to extract the trajectories of unambiguous hand motion.