nils-herrmann/clean_scirepeval huggingface dataset:
Traffic-Optimized Data Placement for Social Media

Social media users are generating data on an unprecedented scale. Distributed storage systems are often used to cope with explosive data growth. Data partitioning and replication are two interrelated data placement issues affecting the interserver traffic caused by user-initiated read and write operations in distributed storage systems. This paper investigates how to minimize the interserver traffic among a cluster of social media servers through joint data partitioning and replication optimization. We formally define the problem and study its hardness. We then propose a traffic-optimized partitioning and replication (TOPR) method to continuously adapt data placement according to various dynamics. Evaluations with real Twitter and LiveJournal social graphs show that TOPR not only reduces the interserver traffic significantly but also saves much storage cost of replication compared to state-of-the-art methods. We also benchmark TOPR against the offline optimum by a binary linear program.
Grid-based local feature bundling for efficient object search and localization

We propose a new grid-based image representation for discriminative visual object search, with the goal to efficiently locate the query object in a large image collection. After extracting local invariant features, we partition the image into non-overlapping rectangular grid cells. Each grid bundles the local features within it and is characterized by a histogram of visual words. Given both positive and negative queries, each grid is assigned a mutual information score to match and locate the query object. This new image representation offers two great benefits for efficient object search: 1) as the grid bundles local features, the spatial contextual information enhances the discriminative matching; and 2) it enables faster object localization by searching visual object in the grid-level image. To evaluate our approach, we perform experiments on a very challenging logo database BelgaLogos  of 10,000 images. The comparison with the state-of-the-art methods highlights the effectiveness of our approach in both accuracy and speed.
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition

Motivated by previous success of using non-parametric methods to recognize objects, e.g., NBNN [2], we extend it to recognize actions using skeletons. Each 3D action is presented by a sequence of 3D poses. Similar to NBNN, our proposed Spatio-Temporal-NBNN applies stage-to-class distance to classify actions. However, ST-NBNN takes the spatio-temporal structure of 3D actions into consideration and relaxes the Naive Bayes assumption of NBNN. Specifically, ST-NBNN adopts bilinear classifiers [19] to identify both key temporal stages as well as spatial joints for action classification. Although only using a linear classifier, experiments on three benchmark datasets show that by combining the strength of both non-parametric and parametric models, ST-NBNN can achieve competitive performance compared with state-of-the-art results using sophisticated models such as deep learning. Moreover, by identifying key skeleton joints and temporal stages for each action class, our ST-NBNN can capture the essential spatio-temporal patterns that play key roles of recognizing actions, which is not always achievable by using end-to-end models.
Optimal spatio-temporal path discovery for video event detection

We propose a novel algorithm for video event detection and localization as the optimal path discovery problem in spatio-temporal video space. By finding the optimal spatio-temporal path, our method not only detects the starting and ending points of the event, but also accurately locates it in each video frame. Moreover, our method is robust to the scale and intra-class variations of the event, as well as false and missed local detections, therefore improves the overall detection and localization accuracy. The proposed search algorithm obtains the global optimal solution with proven lowest computational complexity. Experiments on realistic video datasets demonstrate that our proposed method can be applied to different types of event detection tasks, such as abnormal event detection and walking pedestrian detection.
Invariant multi-scale shape descriptor for object matching and recognition

We propose a novel multi-scale shape descriptor for shape matching and object recognition. The descriptor includes three types of invariants in multiple scales to capture discriminative local and semi-global shape features and the dynamic programming algorithm is employed for shape matching. The experimental results verify that our proposed shape feature is invariant to translation, rotation, scaling, and can well tolerate partial occlusion, articulated variation and intra-class variations. The shape matching and retrieval results on benchmark datasets validate the effectiveness of our method. This method is also applied to real time hand gesture recognition and achieves competitive result compared with state of the arts.
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images

We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (3D CNNs). Image based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN taking a 3D volumetric representation of the hand depth image as input can capture the 3D spatial structure of the input and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. Experiments show that our proposed 3D CNN based approach outperforms state-of-the-art methods on two challenging hand pose datasets, and is very efficient as our implementation runs at over 215 fps on a standard computer with a single GPU.
Learning sparse tag patterns for social image classification

User-generated tags associated with images from social media (e.g., Flickr) provide valuable textual resources for image classification. However, the noisy and huge tag vocabulary heavily degrades the effectiveness and efficiency of state-of-the-art image classification methods that exploited auxiliary web data. To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover sparsity constrained co-occurrence tag patterns from large scale user contributed tags among social data. To fulfill the compactness and discriminability, we formulate STP as a problem of minimizing a quadratic loss function regularized by the bi-layer l1 norm. We treat the learned STP as alternative intermediate semantic image feature and verify its superiority within a search-based image classification framework. Experiments on 240K social images associated with millions of tags have demonstrated encouraging performance of the proposed method compared to the state-of-the-art.
Sparse reconstruction cost for abnormal event detection

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.
Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search

Although sliding window-based approaches have been quite successful in detecting objects in images, it is not a trivial problem to extend them to detecting events in videos. We propose to search for spatiotemporal paths for video event detection. This new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intraclass variations of the event. Compared to event detection using spatiotemporal sliding windows, the spatiotemporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video data sets with different event detection tasks, such as anomaly event detection, walking person detection, and running detection. Our proposed method is compatible with different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-the-art methods.
Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context

Video anomaly detection plays a critical role for intelligent video surveillance. We present an abnormal video event detection system that considers both spatial and temporal contexts. To characterize the video, we first perform the spatio-temporal video segmentation and then propose a new region-based descriptor called “Motion Context,” to describe both motion and appearance information of the spatio-temporal segment. For anomaly measurements, we formulate the abnormal event detection as a matching problem, which is more robust than statistic model-based methods, especially when the training dataset is of limited size. For each testing spatio-temporal segment, we search for its best match in the training dataset, and determine how normal it is using a dynamic threshold. To speed up the search process, compact random projections are also adopted. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.
Efficient tracking of closely spaced objects in depth data using sequential dirichlet process clustering

Many approaches for tracking objects in lidar data have been proposed in recent years. However, most practical real time systems assume that clean segmentation of lidar points into individual objects can be achieved. Unfortunately, efficient lidar segmentation approaches are prone to under-segmentation when objects are very close to each other; one solution is to introduce additional segmentation steps into the tracking process. In this paper we propose a new method to address this task with distance dependent Chinese Restaurant Processes (dd-CRP) equipped with a shape prior defining possible object shapes. By adding constraints to the segmentation model, we are able to further improve stability of segmentation and tracking. Experiments on real datasets show the advantage of this approach over a baseline object tracking pipeline.
Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps

We propose an efficient online video filtering method, called adaptive exponential filtering (AES) to refine pixel prediction maps. Assuming each pixel is associated with a discriminative prediction score, the proposed AES applies exponentially decreasing weights over time to smooth the prediction score of each pixel, similar to classic exponential smoothing. However, instead of fixing the spatial pixel location to perform temporal filtering, we trace each pixel in the past frames by finding the optimal path that can bring the maximum exponential smoothing score, thus performing adaptive and non-linear filtering. Thanks to the pixel tracing, AES can better address object movements and avoid over-smoothing. To enable real-time filtering, we propose a linear-complexity dynamic programming scheme that can trace all pixels simultaneously. We apply the proposed filtering method to improve both saliency detection maps and scene parsing maps. The comparisons with average and exponential filtering, as well as state-of-the-art methods, validate that our AES can effectively refine the pixel prediction maps, without using the original video again.
Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection

Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. The learned part detectors can be further integrated to better detect partially occluded pedestrians. Experiments on the Caltech dataset show state-of-the-art performance of our approach for detecting heavily occluded pedestrians.
Discriminative Action States Discovery for Online Action Recognition

In this paper, we provide an approach for online human action recognition, where the videos are represented by frame-level descriptors. To address the large intraclass variations of frame-level descriptors, we propose an action states discovery method to discover the different distributions of frame-level descriptors while training a classifier. A positive sample set is treated as multiple clusters called action states. The action states model can be effectively learned by clustering the positive samples and optimizing the decision boundary of each state simultaneously. Experimental results show that our method not only outperforms the state-of-the-art methods, but also can predict the video by an on-going process with a real-time speed.
Scalable forest hashing for fast similarity search

Indexing images and videos using binary hash bits has shown promising results for fast similarity search. Existing datadriven hashing methods learn compact hash codes from the data, but usually with the cost of generating unbalanced hash buckets, thus affecting the search efficiency. We propose a novel data-driven hashing method called forest hashing, which utilizes multiple tree structures to perform data hashing. By leveraging the index structure of trees, we can significantly improve the hashing efficacy by generating balanced hash buckets. Moreover, forest hashing naturally supports scalable coding where more trees can improve the coding quality with a longer code. Last but not the least, our forest hashing can be easily extended for semantic search by integrating semi-supervised label information. Experiments on two benchmark datasets show favorable results compared with the state-of-the-art hashing methods.
Multi-feature Spectral Clustering with Minimax Optimization

In this paper, we propose a novel formulation for multi-feature clustering using minimax optimization. To find a consensus clustering result that is agreeable to all feature modalities, our objective is to find a universal feature embedding, which not only fits each individual feature modality well, but also unifies different feature modalities by minimizing their pairwise disagreements. The loss function consists of both (1) unary embedding cost for each modality, and (2) pairwise disagreement cost for each pair of modalities, with weighting parameters automatically selected to maximize the loss. By performing minimax optimization, we can minimize the loss for the worst case with maximum disagreements, thus can better reconcile different feature modalities. To solve the minimax optimization, an iterative solution is proposed to update the universal embedding, individual embedding, and fusion weights, separately. Our minimax optimization has only one global parameter. The superior results on various multi-feature clustering tasks validate the effectiveness of our approach when compared with the state-of-the-art methods.
Fast action proposals for human action detection and search

In this paper we target at generating generic action proposals in unconstrained videos. Each action proposal corresponds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good potential to locate one human action. Assuming each action is performed by a human with meaningful motion, both appearance and motion cues are utilized to measure the actionness of the video tubes. After picking those spatiotemporal paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage problem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search.
Common Action Discovery and Localization in Unconstrained Videos

Similar to common object discovery in images or videos, it is of great interests to discover and locate common actions in videos, which can benefit many video analytics applications such as video summarization, search, and understanding. In this work, we tackle the problem of common action discovery and localization in unconstrained videos, where we do not assume to know the types, numbers or locations of the common actions in the videos. Furthermore, each video can contain zero, one or several common action instances. To perform automatic discovery and localization in such challenging scenarios, we first generate action proposals using human prior. By building an affinity graph among all action proposals, we formulate the common action discovery as a subgraph density maximization problem to select the proposals containing common actions. To avoid enumerating in the exponentially large solution space, we propose an efficient polynomial time optimization algorithm. It solves the problem up to a user specified error bound with respect to the global optimal solution. The experimental results on several datasets show that even without any prior knowledge of common actions, our method can robustly locate the common actions in a collection of videos.
Human–Robot Interaction by Understanding Upper Body Gestures

In this paper, a human–robot interaction system based on a novel combination of sensors is proposed. It allows one person to interact with a humanoid social robot using natural body language. The robot understands the meaning of human upper body gestures and expresses itself by using a combination of body movements, facial expressions, and verbal language. A set of 12 upper body gestures is involved for communication. This set also includes gestures with human–object interactions. The gestures are characterized by head, arm, and hand posture information. The wearable Immersion CyberGlove II is employed to capture the hand posture. This information is combined with the head and arm posture captured from Microsoft Kinect. This is a new sensor solution for human-gesture capture. Based on the posture data from the CyberGlove II and Kinect, an effective and real-time human gesture recognition method is proposed. The gesture understanding approach based on an innovative combination of sensors is the main contribution of this paper. To verify the effectiveness of the proposed gesture recognition method, a human body gesture data set is built. The experimental results demonstrate that our approach can recognize the upper body gestures with high accuracy in real time. In addition, for robot motion generation and control, a novel online motion planning method is proposed. In order to generate appropriate dynamic motion, a quadratic programming (QP)-based dual-arms kinematic motion generation scheme is proposed, and a simplified recurrent neural network is employed to solve the QP problem. The integration of a handshake within the HRI system illustrates the effectiveness of the proposed online generation method.
Bayesian tracking of multiple objects with vision and radar

This paper is concerned with a system for detecting and tracking multiple 3D bounding boxes based on information from multiple sensors. Our framework is built around an inference engine similar to the probability hypothesis density (PHD) filter, where the state space consists of stochastic bounding boxes with constant velocity dynamics. We outline measurement equations for two modalities (vision and radar). The result is a flexible inference system suitable for use on autonomous vehicles.
Video anomaly detection in spatiotemporal context

Compared to other approaches that analyze object trajectories, we propose to detect anomalous video events at three levels considering spatiotemporal context of video objects, i.e., point anomaly, sequential anomaly, and co-occurrence anomaly. A hierarchical data mining approach is proposed to achieve this task. At each level, the frequency based analysis is performed to automatically discover regular rules of normal events. The events deviating from these rules are detected as anomalies. Experiments on real traffic video prove that the detected video anomalies are hazardous or illegal according to the traffic rule.
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos

This paper tackles the problem of efficient and effective object instance search in videos. To effectively capture the relevance between a query and video frames and precisely localize the particular object, we leverage the object proposals to improve the quality of object instance search in videos. However, hundreds of object proposals obtained from each frame could result in unaffordable memory and computational cost. To this end, we present a simple yet effective hierarchical object prototype encoding (HOPE) model to accelerate the object instance search without sacrificing accuracy, which exploits both the spatial and temporal self-similarity property existing in object proposals generated from video frames. We design two types of sphere k-means methods, i.e., spatially-constrained sphere k-means and temporally-constrained sphere k-means to learn frame-level object prototypes and dataset-level object prototypes, respectively. In this way, the object instance search problem is cast to the sparse matrix-vector multiplication problem. Thanks to the sparsity of the codes, both the memory and computational cost are significantly reduced. Experimental results on two video datasets demonstrate that our approach significantly improves the performance of video object instance search over other state-of-the-art fast search schemes.