Encouraging LSTMs to Anticipate Actions Very Early

In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos. As such, it is therefore key to the success of computer vision applications requiring to react as early as possible, such as autonomous navigation. In this paper, we propose a new action anticipation method that achieves high prediction accuracy even in the presence of a very small percentage of a video sequence. To this end, we develop a multi-stage LSTM architecture that leverages context-aware and action-aware features, and introduce a novel loss function that encourages the model to predict the correct class as early as possible. Our experiments on standard benchmark datasets evidence the benefits of our approach; We outperform the state-of-the-art action anticipation methods for early prediction by a relative increase in accuracy of 22.0% on JHMDB-21, 14.0% on UT-Interaction and 49.9% on UCF-101.Comment: 13 Pages, 7 Figures, 11 Tables. Accepted in ICCV 2017. arXiv admin note: text overlap with arXiv:1611.0552

Knowledge Creation and Sharing in Organisational Contexts: A Motivation-Based Perspective

This paper develops a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing, and the ways in which different motivational mechanisms interact to foster knowledge sharing and creation in different organisational contexts. The core assumption is that the willingness of organisational members to engage in knowledge sharing can be viewed on a continuum from purely opportunistic behaviour regulated by extrinsic incentives to an apparently altruistic stance fostered by social norms and group identity. The analysis builds on a three-category taxonomy of motivation: adding ‘hedonic’ motivation to the traditional dichotomy of extrinsic and intrinsic motivation. Based on an analysis of empirical case studies in the literature, we argue that the interaction and mix of the three different motivators play a key role in regulating and translating potential into actual behaviour, and they underline the complex dynamics of knowledge sharing and creation in different organisational contexts

Describing Videos by Exploiting Temporal Structure

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.Comment: Accepted to ICCV15. This version comes with code release and supplementary materia

Am I Done? Predicting Action Progress in Videos

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets

Knowledge Creation and Sharing in Organisational Contexts: A Motivation-Based Perspective

This paper develops a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing, and the ways in which different motivational mechanisms interact to foster knowledge sharing and creation in different organisational contexts. The core assumption is that the willingness of organisational members to engage in knowledge sharing can be viewed on a continuum from purely opportunistic behaviour regulated by extrinsic incentives to an apparently altruistic stance fostered by social norms and group identity. The analysis builds on a three-category taxonomy of motivation: adding ‘hedonic’ motivation to the traditional dichotomy of extrinsic and intrinsic motivation. Based on an analysis of empirical case studies in the literature, we argue that the interaction and mix of the three different motivators play a key role in regulating and translating potential into actual behaviour, and they underline the complex dynamics of knowledge sharing and creation in different organisational contexts.Knowledge sharing; tacit knowledge; motivation; incentives; organizational learning; human resource practices