A Comparative Review of Recent Few-Shot Object Detection Algorithms

• URL: http://arxiv.org/abs/2111.00201v1
• Date: Sat, 30 Oct 2021 07:57:11 GMT
• Title: A Comparative Review of Recent Few-Shot Object Detection Algorithms
• Authors: Leng Jiaxu, Chen Taiyue, Gao Xinbo, Yu Yongtao, Wang Ye, Gao Feng, Wang Yue
• Abstract summary: Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem. Recent studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data and the urgent demands to cut costs of data collection and annotation. Recently, some studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions. This survey provides a comprehensive overview from current classic and latest achievements for few-shot object detection to future research expectations from manifold perspectives. In particular, we first propose a data-based taxonomy of the training data and the form of corresponding supervision which are accessed during the training stage. Following this taxonomy, we present a significant review of the formal definition, main challenges, benchmark datasets, evaluation metrics, and learning strategies. In addition, we present a detailed investigation of how to interplay the object detection methods to develop this issue systematically. Finally, we conclude with the current status of few-shot object detection, along with potential research directions for this field.

Related papers

• Beyond Few-shot Object Detection: A Detailed Survey [25.465534270637523]
  Researchers have introduced few-shot object detection (FSOD) approaches that merge few-shot learning and object detection principles. These approaches play a vital role in reducing the reliance on extensive labeled datasets. This survey paper aims to provide a comprehensive understanding of the above-mentioned few-shot settings and explore the methodologies for each FSOD task.
  arXiv  Detail & Related papers  (2024-08-26T13:09:23Z)
• Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
  We discuss the recent advances in deep learning-based object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T14:44:22Z)
• Few-Shot Object Detection: Research Advances and Challenges [15.916463121997843]
  Few-shot object detection (FSOD) combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years.
  arXiv  Detail & Related papers  (2024-04-07T03:37:29Z)
• Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
  Oriented object detection is one of the most fundamental and challenging tasks in remote sensing. Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
  arXiv  Detail & Related papers  (2023-02-21T06:31:53Z)
• Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning [60.64535309016623]
  We propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision. We further introduce a incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-05-09T05:08:08Z)
• Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison [54.357707168883024]
  Few-Shot Object Detection (FSOD) mimics the humans' ability of learning to learn. FSOD intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. We give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols.
  arXiv  Detail & Related papers  (2022-03-27T04:11:28Z)
• Few-Shot Object Detection: A Survey [4.266990593059534]
  Few-shot object detection aims to learn from few object instances of new categories in the target domain. We categorize approaches according to their training scheme and architectural layout. We introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results.
  arXiv  Detail & Related papers  (2021-12-22T07:08:53Z)
• A Survey of Self-Supervised and Few-Shot Object Detection [19.647681501581225]
  Self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Few-shot object detection is about training a model on novel (unseen) object classes with little data. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection.
  arXiv  Detail & Related papers  (2021-10-27T18:55:47Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Incremental Few-Shot Object Detection [96.02543873402813]
  OpeN-ended Centre nEt is a detector for incrementally learning to detect class objects with few examples. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples.
  arXiv  Detail & Related papers  (2020-03-10T12:56:59Z)

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.