Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition

Recently, skeleton-based human action has become a hot research topic because the compact representation of human skeletons brings new blood to this research domain. As a result, researchers began to notice the importance of using RGB or other sensors to analyze human action by extracting skeleton information. Leveraging the rapid development of deep learning (DL), a significant number of skeleton-based human action approaches have been presented with fine-designed DL structures recently. However, a well-trained DL model always demands high-quality and sufficient data, which is hard to obtain without costing high expenses and human labor. In this paper, we introduce a novel data augmentation method for skeleton-based action recognition tasks, which can effectively generate high-quality and diverse sequential actions. In order to obtain natural and realistic action sequences, we propose denoising diffusion probabilistic models (DDPMs) that can generate a series of synthetic action sequences, and their generation process is precisely guided by a spatial-temporal transformer (ST-Trans). Experimental results show that our method outperforms the state-of-the-art (SOTA) motion generation approaches on different naturality and diversity metrics. It proves that its high-quality synthetic data can also be effectively deployed to existing action recognition models with significant performance improvement

From Line Drawings to Human Actions: Deep Neural Networks for Visual Data Representation

In recent years, deep neural networks have been very successful in computer vision, speech recognition, and artificial intelligent systems. The rapid growth of data and fast increasing computational tools provide solid foundations for the applications which rely on the learning of large scale deep neural networks with millions of parameters. The deep learning approaches have been proved to be able to learn powerful representations of the inputs in various tasks, such as image classification, object recognition, and scene understanding. This thesis demonstrates the generality and capacity of deep learning approaches through a series of case studies including image matching and human activity understanding. In these studies, I explore the combinations of the neural network models with existing machine learning techniques and extend the deep learning approach for each task. Four related tasks are investigated: 1) image matching through similarity learning; 2) human action prediction; 3) finger force estimation in manipulation actions; and 4) bimodal learning for human action understanding. Deep neural networks have been shown to be very efficient in supervised learning. Further, in some tasks, one would like to group the features of the samples in the same category close to each other, in additional to the discriminative representation. Such kind of properties is desired in a number of applications, such as semantic retrieval, image quality measurement, and social network analysis, etc. My first study is to develop a similarity learning method based on deep neural networks for image matching between sketch images and 3D models. In this task, I propose to use Siamese network to learn similarities of sketches and develop a novel method for sketch based 3D shape retrieval. The proposed method can successfully learn the representations of sketch images as well as the similarities, then the 3D shape retrieval problem can be solved with off-the-shelf nearest neighbor methods. After studying the representation learning methods for static inputs, my focus turns to learning the representations of sequential data. To be specific, I focus on manipulation actions, because they are widely used in the daily life and play important parts in the human-robot collaboration system. Deep neural networks have been shown to be powerful to represent short video clips [Donahue et al., 2015]. However, most existing methods consider the action recognition problem as a classification task. These methods assume the inputs are pre-segmented videos and the outputs are category labels. In the scenarios such as the human-robot collaboration system, the ability to predict the ongoing human actions at an early stage is highly important. I first attempt to address this issue with a fast manipulation action prediction method. Then I build the action prediction model based on Long Short-Term Memory (LSTM) architecture. The proposed approach processes the sequential inputs as continuous signals and keeps updating the prediction of the intended action based on the learned action representations. Further, I study the relationships between visual inputs and the physical information, such as finger forces, that involved in the manipulation actions. This is motivated by recent studies in cognitive science which show that the subject’s intention is strongly related to the hand movements during an action execution. Human observers can interpret other’s actions in terms of movements and forces, which can be used to repeat the observed actions. If a robot system has the ability to estimate the force feedbacks, it can learn how to manipulate an object by watching human demonstrations. In this work, the finger forces are estimated by only watching the movement of hands. A modified LSTM model is used to regress the finger forces from video frames. To facilitate this study, a specially designed sensor glove has been used to collect data of finger forces, and a new dataset has been collected to provide synchronized streams of videos and finger forces. Last, I investigate the usefulness of physical information in human action recognition, which is an application of bimodal learning, where both the vision inputs and the additional information are used to learn the action representation. My study demonstrates that, by combining additional information with the vision inputs, the accuracy of human action recognition can be improved steadily. I extend the LSTM architecture to accept both video frames and sensor data as bimodal inputs to predict the action. A hallucination network is jointly trained to approximate the representations of the additional inputs. During the testing stage, the hallucination network generates approximated representations that used for classification. In this way, the proposed method does not rely on the additional inputs for testing

Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative