In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video action recognition. In contrast to traditional methods which generally used entire images, we propose a new human instance-level video action recognition framework. In this framework, we represent the instance-level features using human boxes and keypoints, and our action region features are used as the inputs of the temporal action head network, which makes our framework more discriminative. We also propose novel temporal action head networks consisting of various modules, which reflect various temporal dynamics well. In the experiment, the proposed models achieve comparable performance with the state-of-the-art approaches on two challenging datasets. Furthermore, we evaluate the proposed features and networks to verify the effectiveness of them. Finally, we analyze the confusion matrix and visualize the recognized actions at human instance level when there are several people.

In recent years, the dance entertainment industry has experienced significant growth, driven by the desire of consumers to learn and improve their dancing skills. To effectively improve their skills, dancers require evaluation and feedback, which traditionally relies heavily on professional dancers. To address this challenge, researchers have proposed objective assessment methods for dance performance via kinematic data captured by sensors. However, these existing methods primarily focus on assessing the rhythmic accuracy of movements synchronized to music. In this paper, we propose Dance Quality Assessment (DanceQA) Framework to evaluate dance performance, considering choreographic factors that are important criteria in subjective DanceQA. We find that kinematic diversity and rhythmic alignment are significant choreographic factors from human perception perspective. Based on these factors, we design two metrics: kinematic information entropy (KIE) and kinematic-music beat similarity (BSIM). Our study demonstrates that these metrics are closely related to specific body parts in each choreography. To validate the effectiveness of our metrics, we capture dance performance by OptiTrack system providing precise three-dimensional data at very high sampling rate. We then label their dance quality via subjective test. The metrics give strong correlation with subjective opinion, but it is difficult to tell which body part is the most correlated. To comprehensively understand the dance quality, we propose choreographic quality transformers (CQTs), which learn the aforementioned choreographic factors by embedding KIE and BSIM into attention matrices. In numerous experiments, the CQTs outperforms previous methods, graph convolutional networks and multimodal transformers, at least by up to 0.146 in correlation coefficient.

Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing studies on weighted erasable itemset mining do not regard the time-sensitivity of data arrival times. In this paper, we propose a new weighted erasable itemset mining approach considering time-sensitive dynamic environments. For industrial manufacturers with automated control systems, our method focuses on recent data and item weights to effectively filter out unprofitable itemsets. Performance tests show that the proposed method outperforms state-of-the-art studies in runtime with on-par or minimal compromise in memory usage and that it scales capably with varying database sizes. We performed statistical analyses to demonstrate the correctness and significance of the discovered results from our proposed method. Furthermore, extended evaluations on sensitivity and resultant itemsets show how the algorithm responds to varying parameters as well as provide insights on the discovered itemsets.

In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of infringements has been increasing continuously. To uncover these infringements and handle big data extracted from copyright photos, we propose a photo copyright identification framework to accurately handle manipulations of stolen photos. From a collage of cropped photos, regions of interest (RoIs) are detected to reduce the influence of cropping and identify each photo by Image RoI Detection. Binary descriptors for quick database search are generated from the RoIs by Image Hashing robustly to geometric and color manipulations. The matching results of Image Hashing are verified by measuring their similarity using the proposed Image Verification to reduce false positives. Experimental results demonstrate that the proposed framework outperforms other image retrieval methods in identification accuracy and significantly reduces the false positive rate by 2.8%. This framework is expected to identify copyright infringements in practical situations and have a positive effect on the copyright market.

The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides action sequences in actual environments and helps to handle a network generalization issue due to the dataset shift. When the action recognition model is trained on the ETRI-Activity3D and KIST SynADL datasets and evaluated on the ETRI-Activity3D-LivingLab dataset, the performance can be severely degraded because the datasets were captured in different environments domains. To reduce this dataset shift between training and testing datasets, we propose a close-up of maximum activation, which magnifies the most activated part of a video input in detail. In addition, we present various experimental results and analysis that show the dataset shift and demonstrate the effectiveness of the proposed method.