130,779 research outputs found
An end-to-end administrative document analysis system
International audienceThis paper presents an end-to-end administrative document analysis system. This system uses case-based reasoning in order to process documents from known and unknown classes. For each document, the system retrieves the nearest processing experience in order to analyze and interpret the current document. When a complete analysis is done, this document needs to be added to the document database. This requires an incremental learning process in order to take into account every new information, without losing the previous learnt ones. For this purpose, we proposed an improved version of an already existing neural network called Incremental Growing Neural Gas. Applied on documents learning and classification, this neural network reaches a recognition rate of 97.63%
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
In this paper we describe the design and the ideas motivating a new Continual
Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems
of object classification and object detection. The benchmark utilises SODA10M,
a recently released large-scale dataset that concerns autonomous driving
related problems. First, we review and discuss existing continual learning
benchmarks, how they are related, and show that most are extreme cases of
continual learning. To this end, we survey the benchmarks used in continual
learning papers at three highly ranked computer vision conferences. Next, we
introduce CLAD-C, an online classification benchmark realised through a
chronological data stream that poses both class and domain incremental
challenges; and CLAD-D, a domain incremental continual object detection
benchmark. We examine the inherent difficulties and challenges posed by the
benchmark, through a survey of the techniques and methods used by the top-3
participants in a CLAD-challenge workshop at ICCV 2021. We conclude with
possible pathways to improve the current continual learning state of the art,
and which directions we deem promising for future research
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