Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Shao, Feifei; Chen, Long; Shao, Jian; Ji, Wei; Xiao, Shaoning; Lü, Ye; Zhuang, Yueting; Xiao, Jun

doi:10.48550/arxiv.2105.12694

Cited by 4 publications

(3 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4) Weakly Supervised Object Detection relaxes the required annotations such that the training data contain only imagelevel labels, i.e., whether a specific object category is present or absent somewhere in the image [29], [30], [31]. These annotations are much easier to obtain and can often be acquired by keyword search.…”

Section: B Object Detectionmentioning

confidence: 99%

Few-Shot Object Detection: A Comprehensive Survey

Köhler

Eisenbach

Groß

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learningbased object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection (FSOD) aims to learn from few object instances of new categories in the target domain.In this survey, we provide an overview of the state of the art in FSOD. We categorize approaches according to their training scheme and architectural layout. For each type of approach, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze the reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of FSOD.

show abstract

Section: B Object Detectionmentioning

confidence: 99%

Few-Shot Object Detection: A Comprehensive Survey

Köhler

Eisenbach

Groß

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…In such cases, learning-based techniques are used to overcome these issues. The tremendous success of deep learning for various 3D perception tasks [16]- [19] has resulted in the use of deep learning for point cloud registration as well. This can be seen in a number of approaches [3], [20]- [29] that appeared in the recent years.…”

Section: Oursmentioning

confidence: 99%

Point Cloud Registration With Object-Centric Alignment

et al. 2022

View full text Add to dashboard Cite

Point cloud registration is a core task in 3D perception, which aims to align two point clouds. Moreover, the registration of point clouds with low overlap represents a harder challenge, where previous methods tend to fail. Recent deep learning-based approaches attempt to overcome this issue by learning to find overlapping regions in the whole scene. However, they still lack robustness and accuracy, and thus might not be suitable for real-world applications. Therefore, we present a novel registration pipeline that focuses on object-level alignment to provide a robust and accurate alignment of point clouds. By extracting and completing the missing points of the object of interest, a rough alignment can be achieved even for point clouds with low overlap captured from widely apart viewpoints. We provide a quantitative and qualitative evaluation on synthetic and real-world data captured with a Kinect v2. The proposed approach outperforms the current the current state-of-the-art methods by more than 29% w.r.t. the registration recall on the introduced synthetic dataset. We show that the overall performance and robustness increases due to the object-level alignment, while the baselines perform poorly as they take the entire scene into account.

show abstract

“…To avoid the algorithm's dependence on instance-level labels, more and more researchers try to detect objects in images under weakly supervised [6] learning scenarios [7], [8]; that is, only image-level labels are used to complete the training of weakly supervised object detection models, which are called weakly supervised learning (WSL) and weakly supervised object detection (WSOD). And RSI-based WSOD becomes an important task in weakly supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Learning-Based Network for Weakly Supervised Remote Sensing Object Detection

Chen

Wang

Mukherjee

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Existing object detection algorithms rely excessively on instance-level labels, which are both time-consuming and expensive. In particular, for remote sensing images (RSI) with small and dense objects, the labeling cost is much higher than that of general images. Moreover, the propagation process of the labels over the noisy channel results in blurred and noisy information. To address the problem of obtaining RSI instance-level labels, we aim to propose a collaborative learning-based network for weakly supervised remote sensing object detection (CLN-RSOD).Compared with the state-of-the-art, the proposed model combines the advantages of two object detection sub-networks by jointly training. This improves the model's capability and thereby enhances the detection effect for multi-object in RSI.Moreover, we employ a mask-based proposal refinement algorithm for remote sensing images (MPR-RS) to optimize the candidate boxes. In addition, according to the data distribution characteristics of RSI, we introduce a new joint pooling module (JPM) in CLN-RSOD to enhance the backbone network's characterization of RSI. Finally, the experimental results on two public remote sensing datasets illustrate that the proposed weakly supervised learning method is superior to other weakly supervised methods and demonstrates the effectiveness of the proposed CLN-RSOD.

show abstract

Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Cited by 4 publications

References 85 publications

Few-Shot Object Detection: A Comprehensive Survey

Few-Shot Object Detection: A Comprehensive Survey

Point Cloud Registration With Object-Centric Alignment

Collaborative Learning-Based Network for Weakly Supervised Remote Sensing Object Detection

Contact Info

Product

Resources

About