75 research outputs found
Re-examining Distillation For Continual Object Detection
Training models continually to detect and classify objects, from new classes
and new domains, remains an open problem. In this work, we conduct a thorough
analysis of why and how object detection models forget catastrophically. We
focus on distillation-based approaches in two-stage networks; the most-common
strategy employed in contemporary continual object detection work.Distillation
aims to transfer the knowledge of a model trained on previous tasks -- the
teacher -- to a new model -- the student -- while it learns the new task. We
show that this works well for the region proposal network, but that wrong, yet
overly confident teacher predictions prevent student models from effective
learning of the classification head. Our analysis provides a foundation that
allows us to propose improvements for existing techniques by detecting
incorrect teacher predictions, based on current ground-truth labels, and by
employing an adaptive Huber loss as opposed to the mean squared error for the
distillation loss in the classification heads. We evidence that our strategy
works not only in a class incremental setting, but also in domain incremental
settings, which constitute a realistic context, likely to be the setting of
representative real-world problems
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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