Event Detection Dataset Research Articles

A Class-Incremental Learning Method for Interactive Event Detection via Interaction, Contrast and Distillation

Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods.

Nighttime agglomerate fog event detection considering car light glare based on video

Agglomerate fog event poses more serious threat than normal foggy weather to expressway traffic safety, due to its localized nature and suddenly uneven formation. However, vision-based fog detection methods typically estimate visibility for individual images and ignore the difference in the characteristics of even and uneven fog, lacking use of temporal information to differentiate between normal foggy weather and agglomerate fog events. Meanwhile, detection of fog at night faces strong interference from car lights that is always overlooked. This study proposes a nighttime agglomerate fog event detection method for videos, taking into account car light interference. Depth disparity feature is constructed based on the information entropy of depth estimation result, and in order to build a metric for uneven characteristics in the field of view, we creatively introduce the Moran’s index to establish uneven feature, generating two-dimensional feature time series for each video. By extracting interpretable features from the two-dimensional feature time series after removing car light interference frames, a classification model based on XGBoost is built to differentiate agglomerate fog, normal fog, and no fog videos. Experiments are carried out utilizing real monitoring data from roadside surveillance cameras to validate the effectiveness of features and model. Furthermore, a fog event detection dataset containing over 1500 videos is established, making up data scarcity for vision-based agglomerate fog event detection and providing support for future research.

Multimodal semantic enhanced representation network for micro-video event detection

Currently, micro-videos have gained widespread acceptance as a prominent form of user-generated content across various social media platforms. Accurate event analysis of micro-videos can greatly enhance the many diverse social media platforms applications. Although some studies have shown promising results from a multimodal perspective, there is still a challenge in extracting informative cues from inaccurate modalities, particularly for text modality that is prone to inaccuracies and noise. In this paper, we propose a multimodal semantically enhanced representation network (MSERN) for micro-video event detection. To better address inaccurate and noisy text sentences, we first extract visual concepts in the form of adjective-noun pairs (ANPs), through a fine-grained common representation module, to complement the textual descriptions. To maximize the acquisition of modality-specific cues from both visual and textual modalities, we then implement a coarse-grained private representation module to ensure that private representations encompass unique facets of the modalities beyond the common perspective. Finally, because two modules will collaborate, the fine-grained common and coarse-grained private representations are integrated to ensure a reinforced micro-video representation. We evaluate our proposed method on a micro-video event detection dataset and the experimental results show a superior performance compared to the state-of-the-art methods.

Span-based few-shot event detection via aligning external knowledge

Few-shot Event Detection (FSED) aims to identify novel event types in new domains with very limited annotated data. Previous PN-based (Prototypical Network) joint methods suffer from insufficient learning of token-wise label dependency and inaccurate prototypes. To solve these problems, we propose a span-based FSED model, called SpanFSED, which decomposes FSED into two subprocesses, including span extractor and event classifier. In span extraction, we convert sequential labels into a global boundary matrix that enables the span extractor to acquire precise boundary information irrespective of label dependency. In event classification, we align event types with an outside knowledge base like FrameNet and construct an enhanced support set, which injects more trigger information into the prototypical network of event prototypes. The superior performance of SpanFSED is demonstrated through extensive experiments on four event detection datasets, i.e., ACE2005, ERE, MAVEN and FewEvent. Access to our code and data is facilitated through the following link: .

MsPrompt: Multi-step prompt learning for debiasing few-shot event detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm targeting to construct a novel training set that accommodates the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection (FSED), consisting of the following two components: a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the pretrained language models (PLMs) sufficiently for tackling the context-bypassing problem, and a prototypical network module compensating for the weakness of classifying events with sparse data and boost the generalization performance.Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best baseline and achieving an outstanding debiasing performance.

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED , leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE . In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN.

Sound Event Detection System Based on VGGSKCCT Model Architecture with Knowledge Distillation

ABSTRACT Sound event detection involves detecting acoustic events of multiple classes in audio recordings, along with the times of occurrence. Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4 for sound event detection in domestic environments is a contest on this task. In this paper, we engineer sound event detection systems using the data provided and the performance metrics defined in this contest. Note the performance metrics of polyphonic sound detection scores (PSDS) in 2 scenarios are adopted recently to be practical and effective. Our system development started with a basic system through reference to various systems in the contests of previous years. We developed a system similar to that used by the winning team in DCASE Challenge 2021. A clip-level consistency branch is then added to the model architecture to increase the performance of the PSDS in scenario 2, which focuses on identifying different event classes. In addition, we use knowledge distillation with the mean teacher model to improve system performance. In this way, the model can learn from the pre-trained model without being fully restricted by its performance. Finally, we further enhance the system robustness through consistency criteria in the second stage of training. On the official validation set of Domestic Environment Sound Event Detection (DESED) dataset, our final system achieves 0.418 and 0.661 on the PSDS in the two scenarios. It outperforms the 2021 baseline system with 0.341 and 0.546 on both scores quite significantly.

An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems

The Sound Event Detection task aims to determine the temporal locations of acoustic events in audio clips. In recent years, the relevance of this field is rising due to the introduction of datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). In this paper, we analyze the performance of Sound Event Detection systems under diverse artificial acoustic conditions such as high- or low-pass filtering and clipping or dynamic range compression, as well as under an scenario of high overlap between events. For this purpose, the audio was obtained from the Evaluation subset of the DESED dataset, whereas the systems were trained in the context of the DCASE Challenge 2020 Task 4. Our systems are based upon the challenge baseline, which consists of a Convolutional-Recurrent Neural Network trained using the Mean Teacher method, and they employ a multiresolution approach which is able to improve the Sound Event Detection performance through the use of several resolutions during the extraction of Mel-spectrogram features. We provide insights on the benefits of this multiresolution approach in different acoustic settings, and compare the performance of the single-resolution systems in the aforementioned scenarios when using different resolutions. Furthermore, we complement the analysis of the performance in the high-overlap scenario by assessing the degree of overlap of each event category in sound event detection datasets.

A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors.

The consumption of tobacco has reached global epidemic proportions and is characterized as the leading cause of death and illness. Among the different ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the most widespread. In this paper, we present a two-step, bottom-up algorithm towards the automatic and objective monitoring of cigarette-based, smoking behavior during the day, using the 3D acceleration and orientation velocity measurements from a commercial smartwatch. In the first step, our algorithm performs the detection of individual smoking gestures (i.e., puffs) using an artificial neural network with both convolutional and recurrent layers. In the second step, we make use of the detected puff density to achieve the temporal localization of smoking sessions that occur throughout the day. In the experimental section we provide extended evaluation regarding each step of the proposed algorithm, using our publicly-available, realistic Smoking Event Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets recorded under semi-controlled and free-living conditions, respectively. In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0.863 for the detection of puffs and an F1-score/Jaccard index equal to 0.878/0.604 towards the temporal localization of smoking sessions during the day. Finally, to gain further insight, we also compare the puff detection part of our algorithm with a similar approach found in the recent literature.

Enhanced Audio Tagging via Multi- to Single-Modal Teacher-Student Mutual Learning

Recognizing ongoing events based on acoustic clues has been a critical yet challenging problem that has attracted significant research attention in recent years. Joint audio-visual analysis can improve the event detection accuracy but may not always be feasible as under many circumstances only audio recordings are available in real-world scenarios. To solve the challenges, we present a novel visual-assisted teacher-student mutual learning framework for robust sound event detection from audio recordings. Our model adopts a multi-modal teacher network based on both acoustic and visual clues, and a single-modal student network based on acoustic clues only. Conventional teacher-student learning performs unsatisfactorily for knowledge transfer from a multi-modality network to a single-modality network. We thus present a mutual learning framework by introducing a single-modal transfer loss and a cross-modal transfer loss to collaboratively learn the audio-visual correlations between the two networks. Our proposed solution takes the advantages of joint audio-visual analysis in training while maximizing the feasibility of the model in use cases. Our extensive experiments on the DCASE17 and the DCASE18 sound event detection datasets show that our proposed method outperforms the state-of-the-art audio tagging approaches.

A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video.

Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly-adopted definition of an abnormal event, that is, a rarely occurring event that typically depends on the surrounding context. Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events. Our framework is composed of an object detector, a set of appearance and motion auto-encoders, and a set of classifiers. Since our framework only looks at object detections, it can be applied to different scenes, provided that normal events are defined identically across scenes and that the single main factor of variation is the background. This makes our method background agnostic, as we rely strictly on objects that can cause anomalies, and not on the background. To overcome the lack of abnormal data during training, we propose an adversarial learning strategy for the auto-encoders. We create a scene-agnostic set of out-of-domain pseudo-abnormal examples, which are correctly reconstructed by the auto-encoders before applying gradient ascent on the pseudo-abnormal examples. We further utilize the pseudo-abnormal examples to serve as abnormal examples when training appearance-based and motion-based binary classifiers to discriminate between normal and abnormal latent features and reconstructions. Furthermore, to ensure that the auto-encoders focus only on the main object inside each bounding box image, we introduce a branch that learns to segment the main object. We compare our framework with the state-of-the-art methods on four benchmark data sets, using various evaluation metrics. Compared to existing methods, the empirical results indicate that our approach achieves favorable performance on all data sets. In addition, we provide region-based and track-based annotations for two large-scale abnormal event detection data sets from the literature, namely ShanghaiTech and Subway.

Event detection of different English data sources based on transfer learning

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

OR-Skip-Net: Outer residual skip network for skin segmentation in non-ideal situations

Skin segmentation is one of the most important tasks for human activity recognition, video monitoring, face detection, hand gesture recognition, content-based detection, adult content filtering, human tracking, and robotic surgeries. Skin segmentation in ideal situations is easy to accomplish because it is with similar backgrounds. However, the skin segmentation in non-ideal situations is complicated due to difficult background illuminations, the presence of skin-like pixels, and environmental changes. The current studies are handling the mentioned challenges by adding the preprocessing stages in their methods, which increases the overall cost of the system. In addition, prevailing segmentation studies have ignored black skin and mainly focused on white skin for their experiments. To deal with skin segmentation in challenging environments irrespective of skin color, and to eliminate the cost of the preprocessing, this paper proposes an outer residual skip connection-based deep convolutional neural network (OR-Skip-Net) which innovatively empowers the features by transferring the direct edge information from the initial layer to the end of the network.Experiments were performed on the following eight open datasets for skin segmentation in different environments: hand gesture recognition dataset, event detection dataset, laboratoire d'informatique en image et systèmes d'information dataset, in-house dataset, UT-interaction dataset, augmented multi-party interaction dataset, Pratheepan dataset, and black skin people dataset. In addition, two other experiments were performed for gland segmentation from colon cancer histology images for the diagnosis of colorectal cancer using the Warwick-QU dataset and for iris segmentation using the Noisy Iris Challenge Evaluation - Part II dataset to explore the possibility of applying our method to different applications. Experimental results showed that the proposed OR-Skip-Net outperformed existing methods in terms of skin, gland, and iris segmentation accuracies.

Shared Multi-View Data Representation for Multi-Domain Event Detection.

Internet platforms provide new ways for people to share experiences, generating massive amounts of data related to various real-world concepts. In this paper, we present an event detection framework to discover real-world events from multiple data domains, including online news media and social media. As multi-domain data possess multiple data views that are heterogeneous, initial dictionaries consisting of labeled data samples are exploited to align the multi-view data. Furthermore, a shared multi-view data representation (SMDR) model is devised, which learns underlying and intrinsic structures shared among the data views by considering the structures underlying the data, data variations, and informativeness of dictionaries. SMDR incorpvarious constraints in the objective function, including shared representation, low-rank, local invariance, reconstruction error, and dictionary independence constraints. Given the data representations achieved by SMDR, class-wise residual models are designed to discover the events underlying the data based on the reconstruction residuals. Extensive experiments conducted on two real-world event detection datasets, i.e., Multi-domain and Multi-modality Event Detection dataset, and MediaEval Social Event Detection 2014 dataset, indicating the effectiveness of the proposed approaches.

A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media

In this paper, we propose a method for event detection on social media, which aims at clustering media items into groups of events based on their textural information as well as available metadata. Our approach is based on distance-dependent Chinese Restaurant Process (ddCRP), a clustering approach resembling Dirichlet process algorithm. Furthermore, we scrutinize the effectiveness of a series of pre-processing steps in improving the detection performance. We experimentally evaluated our method using the Social Event Detection (SED) dataset of MediaEval 2013 benchmarking workshop, which pertains to the discovery of social events and their grouping in event-specific clusters. The obtained results indicate that the proposed method attains very good performance rates compared to existing approaches.

Extracting Key Segments of Videos for Event Detection by Learning From Web Sources

In this paper, we present a novel approach of extracting the key segments for event detection in unconstrained videos. The key segments are automatically extracted by transferring the knowledge learned from Web images and Web videos to consumer videos. We propose an adaptive latent structural support vector machine model, where the locations of key segments in videos are regarded as latent variables due to the unavailability of the ground truth of key-segment locations in training data. In order to alleviate the time-consuming and labor-expensive manual annotation of huge amounts of training videos, a large number of loosely labeled Web images as well as videos are collected from the Web sources. Additionally, a limited number of labeled consumer videos are utilized to guarantee the precision of the model. Considering the semantic diversity of key segments, we learn a set of concepts as the semantic description of key segments and explore the temporal information of concepts to capture the sequential relations between the segments. The concepts are automatically discovered by using Web images and videos with their associated tags and description sentences. Comprehensive experiments on the Columbia's consumer video and the TRECVID 2014 Multimedia Event Detection datasets demonstrate that our method outperforms the state-of-the-art methods.

Video2vec Embeddings Recognize Events When Examples Are Scarce.

This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlations between the words are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the Video2vec embedding using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose an event specific variant of Video2vec to learn a more accurate representation for the words, which are indicative of the event, by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of Video2vec over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition. By its ability to improve predictability of present day audio-visual video features, while at the same time maximizing their semantic descriptiveness, Video2vec leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.

TagBook: A Semantic Video Representation Without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only a few, or even zero, examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pretrained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness, and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.

Event oriented dictionary learning for complex event detection.

Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask lp -norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method.

Complex Event Detection via Event Oriented Dictionary Learning

Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose two novel strategies to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Towards this goal, we leverage training samples of selected concepts from the Semantic Indexing (SIN) dataset with a pool of 346 concepts, into a novel supervised multi-task dictionary learning framework. Extensive experimental results on TRECVID Multimedia Event Detection (MED) dataset demonstrate the efficacy of our proposed method.