This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are replaced by prototypical networks for online adaptation to new classes. These networks produce embeddings vectors for each generated box, which are then compared with class prototypes. The distance between an embedding and a prototype determines the corresponding classification score. The resulting networks are trained in an episodic manner. A new detection task is randomly sampled at each epoch, consisting in detecting only a subset of the classes annotated in the dataset. This training strategy encourages the network to adapt to new classes as it would at test time. In addition, several ideas are explored to improve the proposed method such as a hard negative examples mining strategy and self-supervised clustering for background objects. The performance of our method is assessed on DOTA, a large-scale remote sensing images dataset. The experiments conducted provide a broader understanding of the capabilities of representation learning. It highlights in particular some intrinsic weaknesses for the few-shot object detection task. Finally, some suggestions and perspectives are formulated according to these insights.
Few-Shot Object Detection (FSOD) is a rapidly growing field in computer vision. It consists in finding all occurrences of a given set of classes with only a few annotated examples for each class. Numerous methods have been proposed to address this challenge and most of them are based on attention mechanisms. However, the great variety of classic object detection frameworks and training strategies makes performance comparison between methods difficult. In particular, for attention-based FSOD methods, it is laborious to compare the impact of the different attention mechanisms on performance. This paper aims at filling this shortcoming. To do so, a flexible framework is proposed to allow the implementation of most of the attention techniques available in the literature. To properly introduce such a framework, a detailed review of the existing FSOD methods is firstly provided. Some different attention mechanisms are then reimplemented within the framework and compared with all other parameters fixed.
Understanding which are the key components of a system that distinguish a normal from a cancerous cell has been approached widely in the recent years using machine learning and statistical approaches to detect gene signatures and predict cell growth. Recently, the idea of using gene regulatory and signaling networks, in the form of logic programs, has been introduced in order to detect the mechanisms that control cells state change. Complementary to this, a large literature deals with constraint based methods for analyzing genome-scale metabolic networks. One of the major outcome of these methods concerns the quantitative prediction of growth rates under both given environmental conditions and the presence or absence of a given set of enzymes which catalyze biochemical reactions. It is of high importance to plug logic regulatory models into metabolic networks by using a gene-enzyme logical interaction rule. In this work, our aim is first to review previously proposed logic programs to discover key components in the graph based causal models that distinguish different variants of cell types. These variants represent either cancerous vs. healthy cell types, multiple cancer cell lines, or patients with different treatment response. With these approaches, we can handle experimental data coming from transcriptomic profiles, gene expression micro-arrays or RNAseq, as well as (multiperturbation) phosphoproteomics measurements. In a second part, we deal with the problem of combining both, regulatory and signaling, logic models within metabolic networks. Such a combination allows us to obtain quantitative prediction of tumor cell growth. Our results point to logic program models built for 3 cancer types: Multiple Myeloma, Acute Myeloid Leukemia, and Breast Cancer. Experimental data for
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