川本 一彦

Skeleton-based action recognition models have recently been shown to be vulnerable to adversarial attacks. Compared to adversarial attacks on images, perturbations to skeletons are typically bounded to a lower dimension of approximately 100 per frame. This lower-dimensional setting makes it more difficult to generate imperceptible perturbations. Existing attacks resolve this by exploiting the temporal structure of the skeleton motion so that the perturbation dimension increases to thousands. In this paper, we show that adversarial attacks can be performed on skeleton-based action recognition models, even in a significantly low-dimensional setting without any temporal manipulation. Specifically, we restrict the perturbations to the lengths of the skeleton's bones, which allows an adversary to manipulate only approximately 30 effective dimensions. We conducted experiments on the NTU RGB+D and HDM05 datasets and demonstrate that the proposed attack successfully deceived models with sometimes greater than 90% success rate by small perturbations. Furthermore, we discovered an interesting phenomenon: in our low-dimensional setting, the adversarial training with the bone length attack shares a similar property with data augmentation, and it not only improves the adversarial robustness but also improves the classification accuracy on the original data. This is an interesting counterexample of the trade-off between adversarial robustness and clean accuracy, which has been widely observed in studies on adversarial training in the high-dimensional regime.

Unsupervised domain adaptation, which involves transferring knowledge from a label-rich source domain to an unlabeled target domain, can be used to substantially reduce annotation costs in the field of object detection. In this study, we demonstrate that adversarial training in the source domain can be employed as a new approach for unsupervised domain adaptation. Specifically, we establish that adversarially trained detectors achieve improved detection performance in target domains that are significantly shifted from source domains. This phenomenon is attributed to the fact that adversarially trained detectors can be used to extract robust features that are in alignment with human perception and worth transferring across domains while discarding domain-specific non-robust features. In addition, we propose a method that combines adversarial training and feature alignment to ensure the improved alignment of robust features with the target domain. We conduct experiments on four benchmark datasets and confirm the effectiveness of our proposed approach on large domain shifts from real to artistic images. Compared to the baseline models, the adversarially trained detectors improve the mean average precision by up to 7.7%, and further by up to 11.8% when feature alignments are incorporated. Although our method degrades performance for small domain shifts, quantification of the domain shift based on the Fréchet distance allows us to determine whether adversarial training should be conducted.

In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners’ perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners’ performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves.

Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive. This has encouraged research in zero-shot action recognition (ZSAR). Presently, most ZSAR methods recognize actions according to each video frame. These methods are affected by light, camera angle, and background, and most methods are unable to process time series data. The accuracy of the model is reduced owing to these reasons. In this paper, in order to solve these problems, we propose a three-stream graph convolutional network that processes both types of data. Our model has two parts. One part can process RGB data, which contains extensive useful information. The other part can process skeleton data, which is not affected by light and background. By combining these two outputs with a weighted sum, our model predicts the final results for ZSAR. Experiments conducted on three datasets demonstrate that our model has greater accuracy than a baseline model. Moreover, we also prove that our model can learn from human experience, which can make the model more accurate.

Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential.

We propose a deep multi-task network for recognizing user’s activities using first-person or egocentric images. The proposed network is composed of three subnetworks: action recognition network, object recognition network, and hand segmentation network. The first two networks are trained for recognizing actions and objects composing the activities in a multitask framework. The hand segmentation network is used to directly capture hand appearance in the deep network. In this paper, we experimentally explore the best deep architecture with the three subnetworks. We show that the activity recognition accuracy increases by 5–12% compared to a baseline model.

Generative adversarial networks( GANs) have been successfully applied for generating high quality natural images and have been extended to the generation of RGB videos and 3D volume data. In this paper we consider the task of generating RGB-D videos, which is less extensively studied and still challenging. We explore deep GAN architectures suitable for the task, and develop 4 GAN architectures based on existing video-based GANs. With a facial expression database, we experimentally find that an extended version of the motion and content decomposed GANs, known as MoCoGAN, provides the highest quality RGB-D videos. We discuss several applications of our GAN to content creation and data augmentation, and also discuss its potential applications in behavioral experiments.

The purpose of this research is to construct a pedes-trian tracking model using data assimilation. We use a floor field for expressing behaviors of pedestrians. We acquire pedestrian data and build a new floor field. Next, we try semantic segmentation using the deep convolution neural network (DCNN). We experiment with discriminating a background and pedestrians and consider effectiveness of semantic segmentation. Finally, we compare an accuracy of pedestrian tracking using background subtraction and an accuracy of pedestrian tracking using semantic segmentation.

We investigate the use of kriging, which is a spatial statistical tool in random fields, for modeling pedestrian dynamics with interaction between pedestrians. In particular we focus on a comparison of prediction and interpolation of pedestrian movements and we discuss advantages and disadvantages of these two computations and demonstrate them with publicly available dataset.

本研究では，人物軌跡データを用いて人物移動を模擬する確率的セルオートマトンを学習する方法を提案し，動画像上での複数人物追跡に応用する．確率的セルオートマトンの学習では，対象空間全体で十分かつ密に人物軌跡データを収集することは一般に困難であることから，ディリクレ平滑化を用いてベイズ的にデータの欠損や不足を補う方法を導入する．さらに，人物追跡のために，学習した確率的セルオートマトンによる人物移動シミュレーションを画像を用いて逐次的に更新するための逐次データ同化アルゴリズムを示す．実動画像を用いた追跡実験では，提案する確率的セルオートマトンを用いることにより，データに基づかない方法と比較して追跡性能が向上することを示す．

We propose a method for recognizing first-person activities based on image classification using DCNN fine-tuning. It is well-known that fine-tuning for pretrained DCNN (deep convolutional neural network) provides a high accuracy in the image classification. However, it costs to collect training data of first-person activities with their labels. On the other hand, in first-person activity videos, objects associated with the activity often come in sight and their appearance does not change very much over the video sequences. Therefore, training images of first-person activities handling some objects can be generated without complex processes. Here, in this study we generate training datasets artificially for fine-tuning and apply them to recognizing first-person reading activities. For classification, we use a DCNN model pre-trained on ImageNet and retrain only final layer weights in the model on our artificial datasets. In our experiments, we evaluate F-measure of the DCNN by applying it to the classification of real first-person activity images. As baseline approaches, we also use DCNN models assembled from scratch, conventional NN (nearest neighbor), and SVM (support vector machine). In addition, we consider how the artificially generated images affect DCNN features by visualizing them. The fine-tuned DCNN model has provided the best F-measure 99.1% in the experiments.

This paper proposes a method for learning pedestrian dynamics with kriging, which is a spatial interpolation method in geosciences. Pedestrian dynamics is generally restricted by other pedestrians and its restriction is caused by social interaction between them. In the proposed method, the social interaction is represented by spatio-temporal correlation of pedestrian dynamics and the correlation is estimated by kriging. As an application of the proposed method, the prediction of pedestrian movement is examined and its performance is evaluated with publicly available benchmark dataset. The experimental results show that 10-step ahead prediction is successful with more than 80% trajectories of the datasets if 2.0[m] distance error is allowed.

© 2015, Fuji Technology Press Ltd. All rights reserved. This paper proposes a regression-based method for modeling social interaction of pedestrians. Pedestrian movements generally are restricted by other pedestrians and its restriction is caused by social interaction between them. In the proposed method, the social interaction is represented by spatiotemporal correlation of pedestrian movements and the correlation is estimated by kriging, which is a spatiotemporal regression method. As an application of the proposed method, the prediction of pedestrian movement is examined and its performance is evaluated with publicly available benchmark dataset. The experimental results show that more than 80% successful prediction rate for hundreds of pedestrian trajectories is achieved if 1.5[m] distance error is allowed.

© 2015, Fuji Technology Press Ltd. All rights reserved. This paper validates kernel size parameters of CNN (convolutional neural networks) for omnidirectional images to determine the best parameters for high human detection accuracy and low computational costs for training. The assumed CNN is two hidden layered architecture. The effectiveness of changing kernel sizes of each layer, which are measured as false detection rate, non-detection rate, and training times, are validated based on human detection experiment with real omnidirectional images. The experimental results suggest that larger 1st hidden layer kernel size and 2nd hidden layer kernel size slightly smaller than the 1st layer are recommended.

カメラパラメータを推定することなく，全方位画像という歪みや位置による見えの変化のある画像に対する人検出器を提案する．Deep Convolutional Neural Networkをベースに実現し，さらに，少量の学習サンプルに対して並進・スケーリング・回転・輝度変化の変形を適用することで大量の学習サンプルを生成する手法も提案している．実際に設置されている全方位カメラの画像を用いた人検出実験では，HOGとReal AdaBoostの組み合わせによる人検出器が誤検出率0.001で未検出率77.5％であるのに対し，提案手法が誤検出率0.001における未検出率が28.2％となることを確認している．提案する学習サンプル生成法が精度向上に寄与することや可視化した特徴マップの検出精度との関連性も検証している．これによりカメラパラメータの推定が難しい状況でも全方位画像からの人検出の精度向上を実現する．

A graph based data mining method, which discovers automatically usage patterns from user-to-user and user-to-object interactions in a collaborative learning space, is proposed. The proposal describes mathematically observed users, objects, and their interactions at a given time as a set of graphs (a usage pattern) whose node is a user or an object and edge is assigned depending on a physical distance between two nodes. It is validated that the proposal can provide useful data for interview planning and evidences for interview results. On the validation, detection of frequent local usage patterns, detection of rare spatial layouts among usage patterns, and grouping hours containing similar local usage patterns are demonstrated with the 324 pictures taken at the collaborative learning space in Chiba University Library.

The saliency map has been proposed to identify regions that draw human visual attention. Differences of features from the surroundings are hierarchially computed for an image or an image sequence in multiple resolutions and they are fused in a fully bottom-up manner to obtain a saliency map. A video usually contains sounds, and not only visual stimuli but also auditory stimuli attract human attention. Nevertheless, most conventional methods discard auditory information and image information alone is used in computing a saliency map. This paper presents a method for constructing a visual saliency map by integrating image features with auditory features. We assume a single moving sound source in a video and introduce a sound source feature. Our method detects the sound source feature using the correlation between audio signals and sound source motion, and computes its importance in each frame in a video using an auditory saliency map. The importance is used to fuse the sound source feature with image features to construct a visual saliency map. Experiments using subjects demonstrate that a saliency map by our proposed method reflects human's visual attention more accurately than that by a conventional method.

In this paper, we experimentally evaluate the validity of dimension-reduction methods for the computation of the similarity in pattern recognition. Image pattern recognition uses pattern recognition techniques for the classification of image data. For the numerical achievement of image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. For the accurate achievement of pattern recognition techniques, the dimension reduction of data vectors is an essential methodology, since the time and space complexities of data processing depend on the dimension of data. However, dimension reduction causes information loss of geometrical and topological features of image patterns. The desired dimension-reduction method selects an appropriate low-dimensional subspace that preserves the information used for classification. © 2013 Springer-Verlag.

In this paper, we experimentally evaluate the validity of dimension-reduction methods which preserve topology for image pattern recognition. Image pattern recognition uses pattern recognition techniques for the classification of image data. For the numerical achievement of image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. For the accurate achievement of pattern recognition techniques, the dimension reduction of data vectors is an essential methodology, since the time and space complexities of data processing depend on the dimension of data. However, the dimension reduction causes information loss of geometrical and topological features of image patterns. The desired dimension-reduction method selects an appropriate low-dimensional subspace that preserves the topological information of the classification space.