mlsanityData to 14 Oct 2026

AttZoom: Attention Zoom for Better Visual Features

arXiv 2508.03625 ↗First posted 5 Aug 2025cs.CV cs.AIcited by 4

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Abstract (arXiv)

We present Attention Zoom, a modular and model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs). Unlike traditional attention approaches that require architecture-specific integration, our method introduces a standalone layer that spatially emphasizes high-importance regions in the input. We evaluated Attention Zoom on multiple CNN backbones using CIFAR-100 and TinyImageNet, showing consistent improvements in Top-1 and Top-5 classification accuracy. Visual analyses using Grad-CAM and spatial warping reveal that our method encourages fine-grained and diverse attention patterns. Our results confirm the effectiveness and generality of the proposed layer for improving CCNs with minimal architectural overhead.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 60 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

39 of 60 references.

References · 39

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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27 more
  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  3. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  4. 2020Rethinking Attention with Performerscited by 1,601
  5. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  6. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
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  10. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
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  12. 2020FcaNet: Frequency Channel Attention Networkscited by 179
  13. 2018BAM: Bottleneck Attention Modulecited by 172
  14. 2019Pyramid Feature Attention Network for Saliency detectioncited by 128
  15. 2020SPAN: Spatial Pyramid Attention Network forImage Manipulation Localizationcited by 92
  16. 2019One-Shot Object Detection with Co-Attention and Co-Excitationcited by 67
  17. 2022A General Survey on Attention Mechanisms in Deep Learningcited by 64
  18. 2019Gated Channel Transformation for Visual Recognitioncited by 43
  19. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  20. 2020DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representationscited by 26
  21. 2019SensitiveNets: Learning Agnostic Representations with Application to Face Imagescited by 18
  22. 2024VAAD: Visual Attention Analysis Dashboard applied to e-Learningcited by 12
  23. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 11
  24. 2024Brain-inspired Artificial Intelligence: A Comprehensive Reviewcited by 9
  25. 2024DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learningcited by 7
  26. 2025Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognitioncited by 7
  27. 2020Fusing CNNs and statistical indicators to improve image classificationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)cited by 7
  2. 2026Comparative Study of Domain-adapted VLMs for General Document Visual Question Answeringcited by 3
  3. 2026Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edemacited by 0
  4. 2026VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Runningcited by 0