Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images

• URL: http://arxiv.org/abs/2109.13027v1
• Date: Mon, 27 Sep 2021 13:04:53 GMT
• Title: Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
• Authors: Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui, Hanene Azzag
• Abstract summary: The performance of our method is assessed on DOTA, a large-scale remote sensing images dataset. It highlights in particular some intrinsic weaknesses for the few-shot object detection task.
• Score: 2.8560476609689185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Joint Neural Networks for One-shot Object Recognition and Detection [5.389851588398047]
  This paper presents a novel joint neural networks approach to address the challenging one-shot object recognition and detection tasks. Inspired by Siamese neural networks and state-of-art multi-box detection approaches, the joint neural networks are able to perform object recognition and detection for categories that remain unseen during the training process. The proposed approach achieves 61.41% accuracy for one-shot object recognition on the MiniImageNet dataset and 47.1% mAP for one-shot object detection when trained on the dataset and tested.
  arXiv  Detail & Related papers  (2024-08-01T16:48:03Z)
• Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery [17.156864650143678]
  We develop a few-shot object detector based on a traditional two-stage architecture. A large-scale pre-trained model is used to build class-reference embeddings or prototypes. We perform evaluations on two remote sensing datasets containing challenging and rare objects.
  arXiv  Detail & Related papers  (2024-03-08T15:20:27Z)
• Identification of Novel Classes for Improving Few-Shot Object Detection [12.013345715187285]
  Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class. We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during training to improve FSOD performance. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.
  arXiv  Detail & Related papers  (2023-03-18T14:12:52Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment [33.446875089255876]
  Few-shot object detection (FSOD) aims to detect objects using only few examples. We propose a meta-learning based few-shot object detection method by transferring meta-knowledge learned from data-abundant base classes to data-scarce novel classes.
  arXiv  Detail & Related papers  (2021-04-15T19:01:27Z)
• Few-shot Weakly-Supervised Object Detection via Directional Statistics [55.97230224399744]
  We propose a probabilistic multiple instance learning approach for few-shot Common Object Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD) Our model simultaneously learns the distribution of the novel objects and localizes them via expectation-maximization steps. Our experiments show that the proposed method, despite being simple, outperforms strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.
  arXiv  Detail & Related papers  (2021-03-25T22:34:16Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)
• Restoring Negative Information in Few-Shot Object Detection [27.876368807936732]
  Few-shot learning has recently emerged as a new challenge in the deep learning field. We introduce a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives. Our method substantially improves the state-of-the-art few-shot object detection solutions.
  arXiv  Detail & Related papers  (2020-10-22T13:39:48Z)
• Region Comparison Network for Interpretable Few-shot Image Classification [97.97902360117368]
  Few-shot image classification has been proposed to effectively use only a limited number of labeled examples to train models for new classes. We propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works. We also present a new way to generalize the interpretability from the level of tasks to categories.
  arXiv  Detail & Related papers  (2020-09-08T07:29:05Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection [76.9756607002489]
  We propose a novel webly supervised object detection (WebSOD) method for novel classes. Our proposed method combines bottom-up and top-down cues for novel class detection. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits.
  arXiv  Detail & Related papers  (2020-03-22T03:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.