Deep Learning on Lie Groups for Skeleton-based Action Recognition

In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensionality, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evaluations of the proposed network for standard 3D human action recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods.Comment: Accepted to CVPR 201

Deep learning for extracting protein-protein interactions from biomedical literature

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table

High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks

Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video. Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features. D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model

High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks

Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video. Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features. D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model

Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets

Analyzing Human-Human Interactions: A Survey

Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions