Multimodal Convolutional Neural Networks for Matching Image and Sentence

In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content, and one matching CNN learning the joint representation of image and sentence. The matching CNN composes words to different semantic fragments and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results on benchmark databases of bidirectional image and sentence retrieval demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. Specifically, our proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and Microsoft COCO databases achieve the state-of-the-art performances.Comment: Accepted by ICCV 201

Generation of multi-modal dialogue for a net environment

In this paper an architecture and special purpose markup language for simulated affective face-to-face communication is presented. In systems based on this architecture, users will be able to watch embodied conversational agents interact with each other in virtual locations on the internet. The markup language, or Rich Representation Language (RRL), has been designed to provide an integrated representation of speech, gesture, posture and facial animation

Generic dialogue modeling for multi-application dialogue systems

We present a novel approach to developing interfaces for multi-application dialogue systems. The targeted interfaces allow transparent switching between a large number of applications within one system. The approach, based on the Rapid Dialogue Prototyping Methodology (RDPM) and the Vector Space model techniques from Information Retrieval, is composed of three main steps: (1) producing finalized dia logue models for applications using the RDPM, (2) designing an application interaction hierarchy, and (3) navigating between the applications based on the user's application of interest

Multimodal agent interfaces and system architectures for health and fitness companions

Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. In this paper we present how such multimodal conversational Companions can be implemented to support their owners in various pervasive and mobile settings. In particular, we focus on different forms of multimodality and system architectures for such interfaces

Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation

This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.Comment: Accepted to ICRA 201