External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery

• URL: http://arxiv.org/abs/2107.10504v1
• Date: Thu, 22 Jul 2021 07:50:44 GMT
• Title: External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery
• Authors: Isaac J. Sledge, Christopher D. Toole, Joseph A. Maestri, and Jose C. Principe
• Abstract summary: We propose a memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery. Our framework relies on first removing non-discriminative details from the imagery using a small-scale DenseNet-inspired network. We then cascade the filtered imagery into a novel NeuralRAM-based convolutional matching network, NRMN, for low-shot target recognition.
• Score: 8.767175335575386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery. Our framework relies on first removing non-discriminative details from the imagery using a small-scale DenseNet-inspired network. Doing so simplifies ensuing analyses and permits generalizing from few labeled examples. We then cascade the filtered imagery into a novel NeuralRAM-based convolutional matching network, NRMN, for low-shot target recognition. We employ a small-scale FlowNet, LFN to align and register FLS imagery across local temporal scales. LFN enables target label consensus voting across images and generally improves target detection and recognition rates. We evaluate our framework using real-world FLS imagery with multiple broad target classes that have high intra-class variability and rich sub-class structure. We show that few-shot learning, with anywhere from ten to thirty class-specific exemplars, performs similarly to supervised deep networks trained on hundreds of samples per class. Effective zero-shot learning is also possible. High performance is realized from the inductive-transfer properties of NRMNs when distractor elements are removed.

Related papers

• Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning [86.99944014645322]
  We introduce a novel framework, Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning. We decompose each query image into its high-frequency and low-frequency components, and parallel incorporate them into the feature embedding network. Our framework establishes new state-of-the-art results on multiple cross-domain few-shot learning benchmarks.
  arXiv  Detail & Related papers  (2024-11-03T04:02:35Z)
• Siamese Transformer Networks for Few-shot Image Classification [9.55588609556447]
  Humans exhibit remarkable proficiency in visual classification tasks, accurately recognizing and classifying new images with minimal examples. Existing few-shot image classification methods often emphasize either global features or local features, with few studies considering the integration of both. We propose a novel approach based on the Siamese Transformer Network (STN) Our strategy effectively harnesses the potential of global and local features in few-shot image classification, circumventing the need for complex feature adaptation modules.
  arXiv  Detail & Related papers  (2024-07-16T14:27:23Z)
• Visual Recognition with Deep Nearest Centroids [57.35144702563746]
  We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition. Compared with parametric counterparts, DNC performs better on image classification (CIFAR-10, ImageNet) and greatly boots pixel recognition (ADE20K, Cityscapes)
  arXiv  Detail & Related papers  (2022-09-15T15:47:31Z)
• Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection [54.24966006457756]
  We propose a WSOD framework called the Spatial Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net) SLV-SD Net converges region proposal localization without bounding box annotations. Experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets demonstrate the excellent performance of SLV-SD Net.
  arXiv  Detail & Related papers  (2022-04-14T11:56:19Z)
• Weakly-supervised fire segmentation by visualizing intermediate CNN layers [82.75113406937194]
  Fire localization in images and videos is an important step for an autonomous system to combat fire incidents. We consider weakly supervised segmentation of fire in images, in which only image labels are used to train the network. We show that in the case of fire segmentation, which is a binary segmentation problem, the mean value of features in a mid-layer of classification CNN can perform better than conventional Class Activation Mapping (CAM) method.
  arXiv  Detail & Related papers  (2021-11-16T11:56:28Z)
• New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis [0.1503974529275767]
  This paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. Experiments on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-28T18:10:26Z)
• Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images [2.8560476609689185]
  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.
  arXiv  Detail & Related papers  (2021-09-27T13:04:53Z)
• Disentangled Feature Representation for Few-shot Image Classification [64.40410801469106]
  We propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications. DFR can adaptively decouple the discriminative features that are modeled by the classification branch, from the class-irrelevant component of the variation branch. In general, most of the popular deep few-shot learning methods can be plugged in as the classification branch, thus DFR can boost their performance on various few-shot tasks.
  arXiv  Detail & Related papers  (2021-09-26T09:53:11Z)
• 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)

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.