4 research outputs found
MultIOD: Rehearsal-free Multihead Incremental Object Detector
Class-Incremental learning (CIL) is the ability of artificial agents to
accommodate new classes as they appear in a stream. It is particularly
interesting in evolving environments where agents have limited access to memory
and computational resources. The main challenge of class-incremental learning
is catastrophic forgetting, the inability of neural networks to retain past
knowledge when learning a new one. Unfortunately, most existing
class-incremental object detectors are applied to two-stage algorithms such as
Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe
that the current benchmarks are not realistic, and more effort should be
dedicated to anchor-free and rehearsal-free object detection. In this context,
we propose MultIOD, a class-incremental object detector based on CenterNet. Our
main contributions are: (1) we propose a multihead feature pyramid and
multihead detection architecture to efficiently separate class representations,
(2) we employ transfer learning between classes learned initially and those
learned incrementally to tackle catastrophic forgetting, and (3) we use a
class-wise non-max-suppression as a post-processing technique to remove
redundant boxes. Without bells and whistles, our method outperforms a range of
state-of-the-art methods on two Pascal VOC datasets.Comment: Under review at the WACV 2024 conferenc
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
This paper has been accepted to appear in the Proceedings of the 2022 European Conference on Machine Learning and Data Mining (ECML-PKDD), 19th to the 23rd of September, Grenoble, FranceInternational audienceDetection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, the detection methods are generally validated by assuming a single implicitly known attack strategy, which does not necessarily account for real-life threats. Indeed, this can lead to an overoptimistic assessment of the detectors' performance and may induce some bias in the comparison between competing detection schemes. We propose a novel multi-armed framework, called MEAD, for evaluating detectors based on several attack strategies to overcome this limitation. Among them, we make use of three new objectives to generate attacks. The proposed performance metric is based on the worst-case scenario: detection is successful if and only if all different attacks are correctly recognized. Empirically, we show the effectiveness of our approach. Moreover, the poor performance obtained for state-of-the-art detectors opens a new exciting line of research