Multimodal Man-Machine Interface for Mission Planning

This paper presents a multimodal interface featuring fusion of multiple modalities for natural human-computer interaction. The architecture of the interface and the methods applied are described, and the results of the real-time multimodal fusion are analyzed. The research in progress concerning a mission planning scenario is discussed and other possible future directions are also presented. Keywords Multimodal interfaces, speech recognition, microphonearray, force-feedback tactile glove, gaze tracking, military maps INTRODUCTION Current human-machine communication systems predominantly use keyboard and mouse inputs that inadequately approximate human abilities for communication. More natural communication technologies such as speech, sight and touch, are capable of freeing computer users from the constraints of keyboard and mouse. Although they are not sufficiently advanced to be used individually for robust human-machine communication, they have adequately advanced to serve simul..

TubeR: Tubelet Transformer for Video Action Detection

We propose TubeR: a simple solution for spatio-temporal video action detection. Different from existing methods that depend on either an offline actor detector or hand-designed actor-positional hypotheses like proposals or anchors, we propose to directly detect an action tubelet in a video by simultaneously performing action localization and recognition from a single representation. TubeR learns a set of tubelet-queries and utilizes a tubelet-attention module to model the dynamic spatio-temporal nature of a video clip, which effectively reinforces the model capacity compared to using actor-positional hypotheses in the spatio-temporal space. For videos containing transitional states or scene changes, we propose a context aware classification head to utilize short-term and long-term context to strengthen action classification, and an action switch regression head for detecting the precise temporal action extent. TubeR directly produces action tubelets with variable lengths and even maintains good results for long video clips. TubeR outperforms the previous state-of-the-art on commonly used action detection datasets AVA, UCF101-24 and JHMDB51-21. Code will be available on GluonCV(https://cv.gluon.ai/)