ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures

Multimodal time series classification is an important aspect of human gesture recognition, in which limitations of individual sensors can be overcome by combining data from multiple modalities. In a deep learning pipeline, the attention mechanism further allows for a selective, contextual concentration on relevant features. However, while the standard attention mechanism is an effective tool when working with Natural Language Processing (NLP), it is not ideal when working with temporally- or spatially-sparse multi-modal data. In this paper, we present a novel attention mechanism, Multi-Modal Attention Preconditioning (MMAP). We first demonstrate that MMAP outperforms regular attention for the task of classification of modalities involving temporal and spatial sparsity and secondly investigate the impact of attention in the fusion of radar and optical data for gesture recognition via three specific modalities: dense spatiotemporal optical data, spatially sparse/temporally dense kinematic data, and sparse spatiotemporal radar data. We explore the effect of attention on early, intermediate, and late fusion architectures and compare eight different pipelines in terms of accuracy and their ability to preserve detection accuracy when modalities are missing. Results highlight fundamental differences between late and intermediate attention mechanisms in respect to the fusion of radar and optical data

Gesture Recognition in Robotic Surgery: a Review

OBJECTIVE: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. METHODS: An article search was performed on 5 bibliographic databases with combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. RESULTS: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. CONCLUSION: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. SIGNIFICANCE: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field