14,207 research outputs found
Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local operation computes the response at a position as a
weighted sum of the features at all positions. This building block can be
plugged into many computer vision architectures. On the task of video
classification, even without any bells and whistles, our non-local models can
compete or outperform current competition winners on both Kinetics and Charades
datasets. In static image recognition, our non-local models improve object
detection/segmentation and pose estimation on the COCO suite of tasks. Code is
available at https://github.com/facebookresearch/video-nonlocal-net .Comment: CVPR 2018, code is available at:
https://github.com/facebookresearch/video-nonlocal-ne
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
Assistive systems for persons with cognitive disabilities (e.g. dementia) are
difficult to build due to the wide range of different approaches people can
take to accomplishing the same task, and the significant uncertainties that
arise from both the unpredictability of client's behaviours and from noise in
sensor readings. Partially observable Markov decision process (POMDP) models
have been used successfully as the reasoning engine behind such assistive
systems for small multi-step tasks such as hand washing. POMDP models are a
powerful, yet flexible framework for modelling assistance that can deal with
uncertainty and utility. Unfortunately, POMDPs usually require a very labour
intensive, manual procedure for their definition and construction. Our previous
work has described a knowledge driven method for automatically generating POMDP
activity recognition and context sensitive prompting systems for complex tasks.
We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The
spreadsheet-like result of the analysis does not correspond to the POMDP model
directly and the translation to a formal POMDP representation is required. To
date, this translation had to be performed manually by a trained POMDP expert.
In this paper, we formalise and automate this translation process using a
probabilistic relational model (PRM) encoded in a relational database. We
demonstrate the method by eliciting three assistance tasks from non-experts. We
validate the resulting POMDP models using case-based simulations to show that
they are reasonable for the domains. We also show a complete case study of a
designer specifying one database, including an evaluation in a real-life
experiment with a human actor
GATE -- an Environment to Support Research and Development in Natural Language Engineering
We describe a software environment to support research and development in natural language (NL) engineering. This environment -- GATE (General Architecture for Text Engineering) -- aims to advance research in the area of machine processing of natural languages by providing a software infrastructure on top of which heterogeneous NL component modules may be evaluated and refined individually or may be combined into larger application systems. Thus, GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE will promote reuse of component technology, permit specialisation and collaboration in large-scale projects, and allow for the comparison and evaluation of alternative technologies. The first release of GATE is now available
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