Open-Set Automatic Target Recognition
- URL: http://arxiv.org/abs/2211.05883v1
- Date: Thu, 10 Nov 2022 21:28:24 GMT
- Title: Open-Set Automatic Target Recognition
- Authors: Bardia Safaei, Vibashan VS, Celso M. de Melo, Shuowen Hu, and Vishal
M. Patel
- Abstract summary: Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors.
Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution.
We propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms.
- Score: 52.27048031302509
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic Target Recognition (ATR) is a category of computer vision
algorithms which attempts to recognize targets on data obtained from different
sensors. ATR algorithms are extensively used in real-world scenarios such as
military and surveillance applications. Existing ATR algorithms are developed
for traditional closed-set methods where training and testing have the same
class distribution. Thus, these algorithms have not been robust to unknown
classes not seen during the training phase, limiting their utility in
real-world applications. To this end, we propose an Open-set Automatic Target
Recognition framework where we enable open-set recognition capability for ATR
algorithms. In addition, we introduce a plugin Category-aware Binary Classifier
(CBC) module to effectively tackle unknown classes seen during inference. The
proposed CBC module can be easily integrated with any existing ATR algorithms
and can be trained in an end-to-end manner. Experimental results show that the
proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10
datasets. To the best of our knowledge, this is the first work to address the
open-set classification problem for ATR algorithms. Source code is available
at: https://github.com/bardisafa/Open-set-ATR.
Related papers
- Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection [0.0]
Open set recognition (OSR) aims to bring classification tasks in a situation that is more like reality.
This study provides an algorithm exploring a new representation of feature space to improve classification in OSR tasks.
arXiv Detail & Related papers (2024-05-09T15:15:34Z) - OpenGCD: Assisting Open World Recognition with Generalized Category
Discovery [4.600906853436266]
A desirable open world recognition (OWR) system requires performing three tasks.
We propose OpenGCD that combines three key ideas to solve the above problems sequentially.
Experiments on two standard classification benchmarks and a challenging dataset demonstrate that OpenGCD not only offers excellent compatibility but also substantially outperforms other baselines.
arXiv Detail & Related papers (2023-08-14T04:10:45Z) - Class-Specific Semantic Reconstruction for Open Set Recognition [101.24781422480406]
Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes.
We propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of auto-encoder (AE) and prototype learning.
Results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition.
arXiv Detail & Related papers (2022-07-05T16:25:34Z) - DAAS: Differentiable Architecture and Augmentation Policy Search [107.53318939844422]
This work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them.
Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
arXiv Detail & Related papers (2021-09-30T17:15:17Z) - Bayesian Embeddings for Few-Shot Open World Recognition [60.39866770427436]
We extend embedding-based few-shot learning algorithms to the open-world recognition setting.
We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets.
arXiv Detail & Related papers (2021-07-29T00:38:47Z) - SCARF: Self-Supervised Contrastive Learning using Random Feature
Corruption [72.35532598131176]
We propose SCARF, a technique for contrastive learning, where views are formed by corrupting a random subset of features.
We show that SCARF complements existing strategies and outperforms alternatives like autoencoders.
arXiv Detail & Related papers (2021-06-29T08:08:33Z) - Opening Deep Neural Networks with Generative Models [2.0962464943252934]
We propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pretrained Deep Neural Networks for visual recognition.
The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample.
We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.
arXiv Detail & Related papers (2021-05-20T20:02:29Z) - Open-set Recognition based on the Combination of Deep Learning and
Ensemble Method for Detecting Unknown Traffic Scenarios [0.9711326718689492]
This work proposes a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) for open set recognition of traffic scenarios.
By inheriting the ensemble nature of RF, the vote pattern of all trees combined with extreme value theory is shown to be well suited for detecting unknown classes.
arXiv Detail & Related papers (2021-05-17T06:48:15Z) - Discovering Reinforcement Learning Algorithms [53.72358280495428]
Reinforcement learning algorithms update an agent's parameters according to one of several possible rules.
This paper introduces a new meta-learning approach that discovers an entire update rule.
It includes both 'what to predict' (e.g. value functions) and 'how to learn from it' by interacting with a set of environments.
arXiv Detail & Related papers (2020-07-17T07:38:39Z)
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.