Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition

• URL: http://arxiv.org/abs/2110.12183v1
• Date: Sat, 23 Oct 2021 09:43:36 GMT
• Title: Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
• Authors: Asish Bera, Zachary Wharton, Yonghuai Liu, Nik Bessis and Ardhendu Behera
• Abstract summary: We propose an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modelling subtle changes in images. The framework is evaluated on six diverse benchmark datasets.
• Score: 13.230646408771868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modelling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The ``usefulness'' of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

Related papers

• Vision Eagle Attention: A New Lens for Advancing Image Classification [0.8158530638728501]
  I introduce Vision Eagle Attention, a novel attention mechanism that enhances visual feature extraction using convolutional spatial attention. The model applies convolution to capture local spatial features and generates an attention map that selectively emphasizes the most informative regions of the image. I have integrated Vision Eagle Attention into a lightweight ResNet-18 architecture, demonstrating that this combination results in an efficient and powerful model.
  arXiv  Detail & Related papers  (2024-11-15T20:21:59Z)
• Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications [2.8084568003406316]
  Graph neural networks (GNNs) present a promising alternative to CNNs and transformers in certain image processing applications. One approach involves converting images into nodes by identifying significant keypoints within them. This research sets the stage for expanding GNN applications into various applications, including but not limited to image classification, segmentation, and registration.
  arXiv  Detail & Related papers  (2023-11-30T06:37:02Z)
• Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network [52.29330138835208]
  Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images. We propose MaKeGNN, a sparse attention-based GNN architecture which bypasses non-repeatable keypoints and leverages matchable ones to guide message passing.
  arXiv  Detail & Related papers  (2023-07-04T02:50:44Z)
• SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization [24.286426387100423]
  We propose a method that captures subtle changes by aggregating context-aware features from most relevant image-regions. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.
  arXiv  Detail & Related papers  (2022-09-05T19:43:15Z)
• LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity [49.84167231111667]
  Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image. We introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations.
  arXiv  Detail & Related papers  (2022-04-06T17:48:18Z)
• 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)
• Learning to ignore: rethinking attention in CNNs [87.01305532842878]
  We propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend. Specifically, we propose to explicitly learn irrelevant information in the scene and suppress it in the produced representation.
  arXiv  Detail & Related papers  (2021-11-10T13:47:37Z)
• VOLO: Vision Outlooker for Visual Recognition [148.12522298731807]
  Vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification. We introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO) Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens. Experiments show that our VOLO achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark.
  arXiv  Detail & Related papers  (2021-06-24T15:46:54Z)
• Keypoint-Aligned Embeddings for Image Retrieval and Re-identification [15.356786390476591]
  We propose to align the image embedding with a predefined order of the keypoints. The proposed keypoint aligned embeddings model (KAE-Net) learns part-level features via multi-task learning. It achieves state of the art performance on the benchmark datasets of CUB-200-2011, Cars196 and VeRi-776.
  arXiv  Detail & Related papers  (2020-08-26T03:56:37Z)
• Explicitly Modeled Attention Maps for Image Classification [35.72763148637619]
  Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. We propose a novel self-attention module with explicitly modeled attention-maps using only a single learnable parameter for low computational overhead. Our method achieves an accuracy improvement of up to 2.2% over the ResNet-baselines in ImageNet ILSVRC.
  arXiv  Detail & Related papers  (2020-06-14T11:47:09Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

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.