Recent research in active learning has mostly focused on the simple image classification task. In this paper, we propose novel methods to estimate sample uncertainties for 2D and 3D object detection using Ensem
Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detectio...
Settles, Burr的【Active Learning Literature Survey】—https://minds.wisconsin.edu/bitstream/handle/1793/60660/TR1648.pdf%3Fsequence%3D1%26isAllowed%3Dy文章为经典的主动学习工作进行了总结。上图是经典的基于池的主动学习框架。在每次的主动学习循环中,根据任务模型和无标签数据的信息,查询策略选择最有价值的...
Until now, the predominant approach in implementing AOD algorithms has involved the use of deep q-learning networks(DQNs), with a single discrete action as the output. Nevertheless, these methods exhibit shortcomings in both implementation efficiency and success rate. To address these challenges, an...
Active learning (AL) can help achieve this goal. AL is a framework for identifying the most informative samples in an unlabeled pool of sam- ples for annotation. It has been heavily studied in computer vision for classification [27, 6, 71, 44, 85, 52], segmenta-...
Settles, Burr的【Active Learning Literature Survey】—https://minds.wisconsin.edu/bitstream/handle/1793/60660/TR1648.pdf%3Fsequence%3D1%26isAllowed%3Dy文章为经典的主动学习工作进行了总结。上图是经典的基于池的主动学习框架。在每次的主动学习循环中,根据任务模型和无标签数据的信息,查询策略选择最有价值的...
Deep Active Learning (DeepAL), an approach combining DL and AL to maximize task performance while minimizing the cost of data labeling, has attracted attention and shown great promise for various tasks such as image classification [115, 119], object detection [85, 88], text classification [4,...
In addition to a medium for embodied movement plans, physical bodies are independently capable of implicit computation [14], [15], information storage [16], novelty detection [17], and learning [18]. By harnessing the power of embodiment and morphological computation [19], active learning ...
Here we propose a memristor stochastic gradient Langevin dynamics in situ learning method that uses the stochastic of memristor modulation to learn efficiency, enabling DBAL within the computation-in-memory (CIM) framework. To prove the feasibility and effectiveness of the proposed method, we ...
These approaches further augment the potential of deep learning in this domain. In addition to its role in object detection, wavelet analysis finds application across various domains within the realm of machine vision. Notably, T. Guo et al. [42] harnessed the power of wavelets to enhance ...