nils-herrmann/clean_scirepeval huggingface dataset:
The S-HOCK dataset: Analyzing crowds at the stadium

The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people “interested in watching something specific that they came to see” . This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.
Towards authentic emotion recognition

In human computer interaction, the ultimate goal is to have effortless and natural communication. In the research literature significant effort has been directed toward understanding the functional aspects of the communication. However, it is well known that the functional aspect is insufficient for natural interactions. Indeed, the emotional or affective aspect has been shown in the psychology literature to be at least if not more important. As emotional beings, we interact most comfortably with other emotional beings. We give an overview of our current research toward automatic recognition of human emotions.
A Survey on Learning to Hash

Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.
Color based retrieval and recognition

In content based retrieval, color indexing is one of the most prevalent retrieval methods. Two key problems in color indexing are: (1) determination of the color space, and (2) finding the best distance measure. Most of the attention from the research literature has been focussed on the color model with little or no consideration of the noise models. By focusing on the noise model, we show how to find a distance measure which is optimal in the sense that it maximizes the similarity probability. Our results include experiments in color based retrieval and object recognition. Furthermore, we implement and test several promising algorithms from the research literature as benchmarks.
Learning Personalized Models for Facial Expression Analysis and Gesture Recognition

Facial expression and gesture recognition algorithms are key enabling technologies for human-computer interaction (HCI) systems. State of the art approaches for automatic detection of body movements and analyzing emotions from facial features heavily rely on advanced machine learning algorithms. Most of these methods are designed for the average user, but the assumption “one-size-fits-all” ignores diversity in cultural background, gender, ethnicity, and personal behavior, and limits their applicability in real-world scenarios. A possible solution is to build personalized interfaces, which practically implies learning person-specific classifiers and usually collecting a significant amount of labeled samples for each novel user. As data annotation is a tedious and time-consuming process, in this paper we present a framework for personalizing classification models which does not require labeled target data. Personalization is achieved by devising a novel transfer learning approach. Specifically, we propose a regression framework which exploits auxiliary (source) annotated data to learn the relation between person-specific sample distributions and parameters of the corresponding classifiers. Then, when considering a new target user, the classification model is computed by simply feeding the associated (unlabeled) sample distribution into the learned regression function. We evaluate the proposed approach in different applications: pain recognition and action unit detection using visual data and gestures classification using inertial measurements, demonstrating the generality of our method with respect to different input data types and basic classifiers. We also show the advantages of our approach in terms of accuracy and computational time both with respect to user-independent approaches and to previous personalization techniques.
(Unseen) event recognition via semantic compositionality

Since high-level events in images (e.g. “dinner”, “motorcycle stunt”, etc.) may not be directly correlated with their visual appearance, low-level visual features do not carry enough semantics to classify such events satisfactorily. This paper explores a fully compositional approach for event based image retrieval which is able to overcome this shortcoming. Furthermore, the approach is fully scalable in both adding new events and new primitives. Using the Pascal VOC 2007 dataset, our contributions are the following: (i) We apply the Faceted Analysis-Synthesis Theory (FAST) to build a hierarchy of 228 high-level events. (ii) We show that rule-based classifiers are better suited for compositional recognition of events than SVMs. In addition, rule-based classifiers provide semantically meaningful event descriptions which help bridging the semantic gap. (iii) We demonstrate that compositionality enables unseen event recognition: we can use rules learned from non-visual cues, together with object detectors to get reasonable performance on unseen event categories.
Facial expression recognition from video sequences

Recognizing human facial expression and emotion by computer is an interesting and challenging problem. We propose a method for recognizing emotions through facial expressions displayed in video sequences. We introduce a tree-augmented naive-Bayes (TAN) classifier that learns the dependencies between facial features; we also provide an algorithm for finding the best TAN structure. Our person-dependent and person-independent experiments show that using this TAN structure provides significantly better results than using simpler NB-classifiers.
Connecting Meeting Behavior with Extraversion&#x2014;A Systematic Study

This work investigates the suitability of medium-grained meeting behaviors, namely, speaking time and social attention, for automatic classification of the Extraversion personality trait. Experimental results confirm that these behaviors are indeed effective for the automatic detection of Extraversion. The main findings of our study are that: 1) Speaking time and (some forms of) social gaze are effective indicators of Extraversion, 2) classification accuracy is affected by the amount of time for which meeting behavior is observed, 3) independently considering only the attention received by the target from peers is insufficient, and 4) distribution of social attention of peers plays a crucial role.
Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that with moderate size labeled training sets and large amount of unlabeled data, our method can utilize unlabeled data to improve classification performance. We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set.
Multimodal Personality Recognition in Collaborative Goal-Oriented Tasks

Incorporating research on personality recognition into computers, both from a cognitive as well as an engineering perspective, would facilitate the interactions between humans and machines. Previous attempts on personality recognition have focused on a variety of different corpora (ranging from text to audiovisual data), scenarios (interviews, meetings), channels of communication (audio, video, text), and different subsets of personality traits (out of the five ones from the Big Five Model). Our study uses simple acoustic and visual nonverbal features extracted from multimodal data, which have been recorded in previously uninvestigated scenarios, and consider all five personality traits and not just a subset. First, we look at the human-machine interaction scenario, where we introduce the display of different “collaboration levels.” Second, we look at the contribution of the human-human interaction (HHI) scenario on the emergence of personality traits. Investigating the HHI scenario creates a stronger basis for future human-agents interactions. Our goal is to study, from a computational approach, the emergence degree of the five personality traits in these two scenarios. The results demonstrate the relevance of each of the two scenarios when it comes to the degree of emergence of certain traits and the feasibility to automatically recognize personality under different conditions.
Multi-Feature Fusion via Hierarchical Regression for Multimedia Analysis

Multimedia data are usually represented by multiple features. In this paper, we propose a new algorithm, namely Multi-feature Learning via Hierarchical Regression for multimedia semantics understanding, where two issues are considered. First, labeling large amount of training data is labor-intensive. It is meaningful to effectively leverage unlabeled data to facilitate multimedia semantics understanding. Second, given that multimedia data can be represented by multiple features, it is advantageous to develop an algorithm which combines evidence obtained from different features to infer reliable multimedia semantic concept classifiers. We design a hierarchical regression model to exploit the information derived from each type of feature, which is then collaboratively fused to obtain a multimedia semantic concept classifier. Both label information and data distribution of different features representing multimedia data are considered. The algorithm can be applied to a wide range of multimedia applications and experiments are conducted on video data for video concept annotation and action recognition. Using Trecvid and CareMedia video datasets, the experimental results show that it is beneficial to combine multiple features. The performance of the proposed algorithm is remarkable when only a small amount of labeled training data are available.
How to complete performance graphs in content-based image retrieval: add generality and normalize scope

The performance of a content-based image retrieval (CBIR) system, presented in the form of precision-recall or precision-scope graphs, offers an incomplete overview of the system under study: the influence of the irrelevant items (embedding) is obscured. We propose a comprehensive and well-normalized description of the ranking performance compared to the performance of an ideal retrieval system defined by ground-truth for a large number of predefined queries. We advocate normalization with respect to relevant class size and restriction to specific normalized scope values (the number of retrieved items). We also propose new three and two-dimensional performance graphs for total recall studies in a range of embeddings.
Viewpoint-Consistent 3D Face Alignment

Most approaches to face alignment treat the face as a 2D object, which fails to represent depth variation and is vulnerable to loss of shape consistency when the face rotates along a 3D axis. Because faces commonly rotate three dimensionally, 2D approaches are vulnerable to significant error. 3D morphable models, employed as a second step in 2D+3D approaches are robust to face rotation but are computationally too expensive for many applications, yet their ability to maintain viewpoint consistency is unknown. We present an alternative approach that estimates 3D face landmarks in a single face image. The method uses a regression forest-based algorithm that adds a third dimension to the common cascade pipeline. 3D face landmarks are estimated directly, which avoids fitting a 3D morphable model. The proposed method achieves viewpoint consistency in a computationally efficient manner that is robust to 3D face rotation. To train and test our approach, we introduce the Multi-PIE Viewpoint Consistent database. In empirical tests, the proposed method achieved simple yet effective head pose estimation and viewpoint consistency on multiple measures relative to alternative approaches.
Authentic Facial Expression Analysis

It is argued that for the computer to be able to interact with humans, it needs to havve the communication skills o humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.
A Prototype Learning Framework Using EMD: Application to Complex Scenes Analysis

In the last decades, many efforts have been devoted to develop methods for automatic scene understanding in the context of video surveillance applications. This paper presents a novel nonobject centric approach for complex scene analysis. Similarly to previous methods, we use low-level cues to individuate atomic activities and create clip histograms. Differently from recent works, the task of discovering high-level activity patterns is formulated as a convex prototype learning problem. This problem results in a simple linear program that can be solved efficiently with standard solvers. The main advantage of our approach is that, using as the objective function the Earth Mover's Distance (EMD), the similarity among elementary activities is taken into account in the learning phase. To improve scalability we also consider some variants of EMD adopting L1 as ground distance for 1D and 2D, linear and circular histograms. In these cases, only the similarity between neighboring atomic activities, corresponding to adjacent histogram bins, is taken into account. Therefore, we also propose an automatic strategy for sorting atomic activities. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches, often outperforming them.
Content-based indexing performance: size normalized precision, recall, generality evaluation

Progress in content-based image retrieval (CBIR) is hampered by the lack of good evaluation practice and test-benches. In this paper, we raise the awareness of all the parameters that define a content-based indexing and retrieval method. Extensive ground-truth, 15,324 hand-checked image queries, was developed for a portrait database of gray-level images and their backside studio logo's. Our aim was to clearly demonstrate the diminishing effect of a growing embedding on performance figures, and the establishment of a reliable ranking of several suggested CBIR gray-level indexing methods. This evaluation scheme was used first to optimize a number of parameters defining the detailed workings of each method. The database, standard image queries, ground-truth, and evaluation scripts are offered for inclusion in an evaluation site like Benchathlon.
Simultaneous Ground Metric Learning and Matrix Factorization with Earth Mover's Distance

Non-negative matrix factorization is widely used in pattern recognition as it has been proved to be an effective method for dimensionality reduction and clustering. We propose a novel approach for matrix factorization which is based on Earth Mover's Distance (EMD) as a measure of reconstruction error. Differently from previous works on EMD matrix decomposition, we consider a semi-supervised learning setting and we also propose to learn the ground distance parameters. While few previous works have addressed the problem of ground distance computation, these methods do not learn simultaneously the optimal metric and the reconstruction matrices. We demonstrate the effectiveness of the proposed approach both on synthetic data experiments and on a real world scenario, i.e. addressing the problem of complex video scene analysis in the context of video surveillance applications. Our experiments show that our method allows not only to achieve state-of-the-art performance on video segmentation, but also to learn the relationship among elementary activities which characterize the high level events in the video scene.
It's all about habits: Exploiting multi-task clustering for activities of daily living analysis

Motivated by applications in areas such as patient monitoring, tele-rehabilitation and ambient assisted living, analyzing activities of daily living is an active research topic in computer vision and image processing. In this paper we address the problem of everyday activity recognition from unlabeled data proposing a novel multi-task clustering (MTC) approach. Our intuition is that, when analyzing activities of daily living, we can take advantage of the fact that people tend to perform the same actions in the same environment (e.g. people working in an office environment use to read and write documents). Thus, even if labels are not available, information about typical activities can be exploited in the learning process. Arguing that the tasks of recognizing activities of specific individuals are related, we resort on multi-task learning and rather than clustering the data of each individual separately, we also look for clustering results which are coherent among related tasks. Extensive experimental results show that our method outperforms several state-of-the-art approaches by up to 11% on the Rochester activities of daily living dataset.
Multi-task linear discriminant analysis for multi-view action recognition

Action recognition is a central problem in many practical applications, such as video annotation, video surveillance and human-computer interaction. Most action recognition approaches are currently based on localized spatio-temporal features that can vary significantly when the viewpoint changes. Therefore, the performance rapidly drops when training and test data correspond to different cameras/viewpoints. Recently, Self-Similarity Matrix (SSM) features have been introduced to circumvent this problem. To improve the performance of current SSM-based methods, in this paper we propose a multi-task learning framework for multi-view action recognition where discriminative SSM features are shared among different views. Inspired by the mathematical connection between multivariate linear regression and Linear Discriminant Analysis (LDA), we propose a novel learning algorithm, where a single optimization framework is defined for multi-task multi-class LDA by choosing an appropriate class indicator matrix. Experimental results on the popular IXMAS dataset demonstrate that our approach achieves accurate performance and compares favorably with state-of-the-art methods.
Feature Selection for Multimedia Analysis by Sharing Information Among Multiple Tasks

While much progress has been made to multi-task classification and subspace learning, multi-task feature selection has long been largely unaddressed. In this paper, we propose a new multi-task feature selection algorithm and apply it to multimedia (e.g., video and image) analysis. Instead of evaluating the importance of each feature individually, our algorithm selects features in a batch mode, by which the feature correlation is considered. While feature selection has received much research attention, less effort has been made on improving the performance of feature selection by leveraging the shared knowledge from multiple related tasks. Our algorithm builds upon the assumption that different related tasks have common structures. Multiple feature selection functions of different tasks are simultaneously learned in a joint framework, which enables our algorithm to utilize the common knowledge of multiple tasks as supplementary information to facilitate decision making. An efficient iterative algorithm is proposed to optimize it, whose convergence is guaranteed. Experiments on different databases have demonstrated the effectiveness of the proposed algorithm.
Probabilistic Temporal Subspace Clustering

Subspace clustering is a common modeling paradigm used to identify constituent modes of variation in data with locally linear structure. These structures are common to many problems in computer vision, including modeling time series of complex human motion. However classical subspace clustering algorithms learn the relationships within a set of data without considering the temporal dependency and then use a separate clustering step (e.g., spectral clustering) for final segmentation. Moreover, these, frequently optimization-based, algorithms assume that all observations have complete features. In contrast in real-world applications, some features are often missing, which results in incomplete data and substantial performance degeneration of these approaches. In this paper, we propose a unified non-parametric generative framework for temporal subspace clustering to segment data drawn from a sequentially ordered union of subspaces that deals with the missing features in a principled way. The non-parametric nature of our generative model makes it possible to infer the number of subspaces and their dimension automatically from data. Experimental results on human action datasets demonstrate that the proposed model consistently outperforms other state-of-the-art subspace clustering approaches.