FAME: Feature Activation Map Explanation on Image Classification and Face Recognition

• URL: http://arxiv.org/abs/2605.12017v1
• Date: Tue, 12 May 2026 12:05:00 GMT
• Title: FAME: Feature Activation Map Explanation on Image Classification and Face Recognition
• Abstract summary: deep networks transform local pixel information into more global concepts in a highly obscured manner.<n>We propose Feature Activation Map Explanation (FAME) which combines both worlds by using network gradients to compute changes to the input image.<n>We show that FAME produces attribution maps that are competitive state-of-the-art systems.
• Score: 4.984991053951943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Learning has revolutionized machine learning, reaching unprecedented levels of accuracy, but at the cost of reduced interpretability. Especially in image processing systems, deep networks transform local pixel information into more global concepts in a highly obscured manner. Explainable AI methods for image processing try to shed light on this issue by highlighting the regions of the image that are important for the prediction task. Among these, Class Activation Mapping (CAM) and its gradient-based variants compute attributions based on the feature map and upscale them to the image resolution, assuming that feature map locations are influenced only by underlying regions. Perturbation-based methods, such as CorrRISE, on the other hand, try to provide pixel-level attributions by perturbing the input with fixed patches and checking how the output of the network changes. In this work, we propose Feature Activation Map Explanation (FAME), which combines both worlds by using network gradients to compute changes to the input image, manipulating it in a gradient-driven way rather than using fixed patches. We apply this technique on two common tasks, image classification and face recognition, and show that CAM's above-mentioned assumption does not hold for deeper networks. We qualitatively and quantitively show that FAME produces attribution maps that are competitive state-of-the-art systems. Our code is available: {\footnotesize https://github.com/AIML-IfI/fame.}

Related papers

• PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization [7.869923456842283]
  Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs.<n>Single-step methods can easily result in under- or over-activation due to limited visual ROI saliency in histology images.<n>Two-step approach is sub-optimal because it is constrained to a frozen classifier, limiting the capacity for localization.<n>In this paper, a multi-task approach for WSOL is introduced for simultaneous training of both tasks to address the asynchronous convergence problem.
  arXiv  Detail & Related papers  (2025-03-31T14:18:01Z)
• CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition [73.51329037954866]
  We propose a robust global representation method with cross-image correlation awareness for visual place recognition. Our method uses the attention mechanism to correlate multiple images within a batch. Our method outperforms state-of-the-art methods by a large margin with significantly less training time.
  arXiv  Detail & Related papers  (2024-02-29T15:05:11Z)
• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• Deep Neural Networks Fused with Textures for Image Classification [20.58839604333332]
  Fine-grained image classification is a challenging task in computer vision. We propose a fusion approach to address FGIC by combining global texture with local patch-based information. Our method has attained better classification accuracy over existing methods with notable margins.
  arXiv  Detail & Related papers  (2023-08-03T15:21:08Z)
• Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value [86.69600830581912]
  We develop a novel visual explanation method called Shap-CAM based on class activation mapping. We demonstrate that Shap-CAM achieves better visual performance and fairness for interpreting the decision making process.
  arXiv  Detail & Related papers  (2022-08-07T00:59:23Z)
• Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-based Localization [0.24596929878045565]
  We introduce a new technique to produce visual explanations for the predictions of a CNN. Our method can be applied to any layer of the network, and like Integrated Gradients it is not affected by the problem of vanishing gradients. Compared to Grad-CAM, heatmaps produced by our algorithm are better focused in the areas of interest, and their numerical computation is more stable.
  arXiv  Detail & Related papers  (2022-05-22T18:30:38Z)
• Patch-Based Stochastic Attention for Image Editing [4.8201607588546]
  We propose an efficient attention layer based on the algorithm PatchMatch, which is used for determining approximate nearest neighbors. We demonstrate the usefulness of PSAL on several image editing tasks, such as image inpainting, guided image colorization, and single-image super-resolution.
  arXiv  Detail & Related papers  (2022-02-07T13:42:00Z)
• CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency [61.40511574314069]
  Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. We propose CAMERAS, a technique to compute high-fidelity backpropagation saliency maps without requiring any external priors.
  arXiv  Detail & Related papers  (2021-06-20T08:20:56Z)
• Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification [114.56752624945142]
  We argue that the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. We propose an efficient mini batch sampling method called Graph Sampling (GS) for large-scale metric learning.
  arXiv  Detail & Related papers  (2021-04-04T06:44:15Z)
• Cross-Descriptor Visual Localization and Mapping [81.16435356103133]
  Visual localization and mapping is the key technology underlying the majority of Mixed Reality and robotics systems. We present three novel scenarios for localization and mapping which require the continuous update of feature representations. Our data-driven approach is agnostic to the feature descriptor type, has low computational requirements, and scales linearly with the number of description algorithms.
  arXiv  Detail & Related papers  (2020-12-02T18:19:51Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)

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.