Parameter-Free Average Attention Improves Convolutional Neural Network Performance (Almost) Free of Charge

• URL: http://arxiv.org/abs/2210.07828v1
• Date: Fri, 14 Oct 2022 13:56:43 GMT
• Title: Parameter-Free Average Attention Improves Convolutional Neural Network Performance (Almost) Free of Charge
• Authors: Nils K\"orber (Center for Artificial Intelligence in Public Health Research, Robert Koch Institute, Berlin, Germany)
• Abstract summary: We introduce a parameter-free attention mechanism called PfAAM, that is a simple yet effective module. PfAAM can be plugged into various convolutional neural network architectures with a little computational overhead and without affecting model size. This demonstrates its wide applicability as a general easy-to-use module for computer vision tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual perception is driven by the focus on relevant aspects in the surrounding world. To transfer this observation to the digital information processing of computers, attention mechanisms have been introduced to highlight salient image regions. Here, we introduce a parameter-free attention mechanism called PfAAM, that is a simple yet effective module. It can be plugged into various convolutional neural network architectures with a little computational overhead and without affecting model size. PfAAM was tested on multiple architectures for classification and segmentic segmentation leading to improved model performance for all tested cases. This demonstrates its wide applicability as a general easy-to-use module for computer vision tasks. The implementation of PfAAM can be found on https://github.com/nkoerb/pfaam.

Related papers

• Neural Clustering based Visual Representation Learning [61.72646814537163]
  Clustering is one of the most classic approaches in machine learning and data analysis. We propose feature extraction with clustering (FEC), which views feature extraction as a process of selecting representatives from data. FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives.
  arXiv  Detail & Related papers  (2024-03-26T06:04:50Z)
• NiNformer: A Network in Network Transformer with Token Mixing Generated Gating Function [1.3812010983144802]
  The attention mechanism was utilized in computer vision as the Vision Transformer ViT. It comes with the drawback of being expensive and requiring datasets of considerable size for effective optimization. This paper introduces a new computational block as an alternative to the standard ViT block that reduces the compute burdens.
  arXiv  Detail & Related papers  (2024-03-04T19:08:20Z)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• Vision Transformer with Convolutions Architecture Search [72.70461709267497]
  We propose an architecture search method-Vision Transformer with Convolutions Architecture Search (VTCAS) The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture. It enhances the robustness of the neural network for object recognition, especially in the low illumination indoor scene.
  arXiv  Detail & Related papers  (2022-03-20T02:59:51Z)
• Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions [0.0]
  We focus on two forms of attention mechanisms: attention modules and self-attention. Attention modules are used to reweight the features of each layer input tensor. Self-Attention, originally proposed in the area of Natural Language Processing makes it possible to relate all the items in an input sequence.
  arXiv  Detail & Related papers  (2021-12-23T18:02:48Z)
• TDAN: Top-Down Attention Networks for Enhanced Feature Selectivity in CNNs [18.24779045808196]
  We propose a lightweight top-down (TD) attention module that iteratively generates a "visual searchlight" to perform top-down channel and spatial modulation of its inputs. Our models are more robust to changes in input resolution during inference and learn to "shift attention" by localizing individual objects or features at each computation step without any explicit supervision.
  arXiv  Detail & Related papers  (2021-11-26T12:35:17Z)
• DMSANet: Dual Multi Scale Attention Network [0.0]
  We propose a new attention module that not only achieves the best performance but also has lesser parameters compared to most existing models. Our attention module can easily be integrated with other convolutional neural networks because of its lightweight nature.
  arXiv  Detail & Related papers  (2021-06-13T10:31:31Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Visual Concept Reasoning Networks [93.99840807973546]
  A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. We propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. Our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
  arXiv  Detail & Related papers  (2020-08-26T20:02:40Z)
• Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images [24.35779077001839]
  We propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations. We introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism.
  arXiv  Detail & Related papers  (2020-01-09T07:47:51Z)

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.