A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval

• URL: http://arxiv.org/abs/2110.04479v1
• Date: Sat, 9 Oct 2021 06:51:00 GMT
• Title: A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval
• Abstract summary: We propose a feature consistency driven attention erasing network (FCAENet) for fine-grained image retrieval. For the first issue, we propose an adaptive augmentation module in FCAENet, which is selective region erasing module (SREM) SREM makes the network more robust on subtle differences of fine-grained task by adaptively covering some regions of raw images. We conduct extensive experiments on five fine-grained benchmark datasets (CUB2011, Aircraft, NABirds, VegFru, Food101) for 12bits, 24bits, 32bits, 48bits hash code.
• Score: 23.76079577951216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale fine-grained image retrieval has two main problems. First, low dimensional feature embedding can fasten the retrieval process but bring accuracy reduce due to overlooking the feature of significant attention regions of images in fine-grained datasets. Second, fine-grained images lead to the same category query hash codes mapping into the different cluster in database hash latent space. To handle these two issues, we propose a feature consistency driven attention erasing network (FCAENet) for fine-grained image retrieval. For the first issue, we propose an adaptive augmentation module in FCAENet, which is selective region erasing module (SREM). SREM makes the network more robust on subtle differences of fine-grained task by adaptively covering some regions of raw images. The feature extractor and hash layer can learn more representative hash code for fine-grained images by SREM. With regard to the second issue, we fully exploit the pair-wise similarity information and add the enhancing space relation loss (ESRL) in FCAENet to make the vulnerable relation stabler between the query hash code and database hash code. We conduct extensive experiments on five fine-grained benchmark datasets (CUB2011, Aircraft, NABirds, VegFru, Food101) for 12bits, 24bits, 32bits, 48bits hash code. The results show that FCAENet achieves the state-of-the-art (SOTA) fine-grained retrieval performance compared with other methods.

Related papers

• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
  We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:50:37Z)
• Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashing [1.6244541005112747]
  Fine-grained hashing is more challenging than traditional hashing problems. We propose a cascaded network to learn compact and highly semantic hash codes. We also propose a novel approach to coordinately balance the loss of multi-task learning.
  arXiv  Detail & Related papers  (2023-03-20T17:08:48Z)
• Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary Encoding [1.3649494534428745]
  A Cross-Scale Context Extracted Hashing Network (CSCE-Net) is proposed in this paper. CSCE-Net learns a content-related Dynamic Sign Function (DSF) to replace the original simple sign function. Our CSCE-Net is context-sensitive and able to perform well on accurate image binary encoding.
  arXiv  Detail & Related papers  (2022-10-14T06:52:40Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Asymmetric Hash Code Learning for Remote Sensing Image Retrieval [22.91678927865952]
  We propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for remote sensing image retrieval. The AHCL generates the hash codes of query and database images in an asymmetric way. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-01-15T07:00:38Z)
• Combined Depth Space based Architecture Search For Person Re-identification [70.86236888223569]
  We aim to design a lightweight and suitable network for person re-identification (ReID) We propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network architecture, which we call CDNet. We then propose a low-cost search strategy named the Top-k Sample Search strategy to make full use of the search space and avoid trapping in local optimal result.
  arXiv  Detail & Related papers  (2021-04-09T02:40:01Z)
• Deep Triplet Hashing Network for Case-based Medical Image Retrieval [33.21919320742157]
  We propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes. We show that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods.
  arXiv  Detail & Related papers  (2021-01-29T01:35:46Z)
• Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
  We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net) We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
  arXiv  Detail & Related papers  (2020-08-06T15:02:38Z)
• ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval [43.41089241581596]
  We study the novel fine-grained hashing topic to generate compact binary codes for fine-grained images. We propose a unified end-to-end trainable network, termed as ExchNet. Our proposal consistently outperforms state-of-the-art generic hashing methods on five fine-grained datasets.
  arXiv  Detail & Related papers  (2020-08-04T07:01:32Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

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.