Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection
In this paper, we presented a preliminary study for tactical driver behavior detection from untrimmed naturalistic driving recordings. While supervised learning based detection is a common approach, it suffers when labeled data is scarce. Manual annotation is both time-consuming and expensive. To emphasize this problem, we experimented on a 104-hour real-world naturalistic driving dataset with a set of predefined driving behaviors annotated. There are three challenges in the dataset. First, predefined driving behaviors are sparse in a naturalistic driving setting. Second, the distribution of driving behaviors is long-tail. Third, a huge intra-class variation is observed. To address these issues, recent self-supervised and supervised learning and fusion of multimodal cues are leveraged into our architecture design. Preliminary experiments and discussions are reported.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-supervised learning for joint SAR and multispectral land cover classification
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In this paper, we present a framework and …
Land Cover ClassificationSelf-Supervised LearningSemi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images. However, these images suffer from the problem of curse of dimensional…
Pseudo LabelS4L: Self-Supervised Semi-Supervised Learning
This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual repres…
General Classificationimage-classificationImage ClassificationRepresentation Learning+1Iterative Graph Self-Distillation
Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level rep…
Contrastive LearningGraph LearningKnowledge DistillationColor-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization
This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-sup…
Colorizationimage-classificationImage ClassificationImage Colorization+1