BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

• URL: http://arxiv.org/abs/2309.03509v1
• Date: Thu, 7 Sep 2023 06:45:43 GMT
• Title: BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications
• Authors: Jiatai Lin, Guoqiang Han, Xuemiao Xu, Changhong Liang, Tien-Tsin Wong, C. L. Philip Chen, Zaiyi Liu, Chu Han
• Abstract summary: We propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. By evaluating BroadCAM on VOC2012 and BCSS-WSSS for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance.
• Score: 69.22739434619531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class activation mapping~(CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation~(WSSS) and object localization~(WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores~(forward information), gradients~(backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5\%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data.

Related papers

• KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA [1.5550533143704957]
  This research introduces KPCA-CAM, a technique designed to enhance the interpretability of Convolutional Neural Networks (CNNs) KPCA-CAM leverages Principal Component Analysis (PCA) with the kernel trick to capture nonlinear relationships within CNN activations more effectively. Empirical evaluations on the ILSVRC dataset across different CNN models demonstrate that KPCA-CAM produces more precise activation maps.
  arXiv  Detail & Related papers  (2024-09-30T22:36:37Z)
• Generalizing GradCAM for Embedding Networks [0.0]
  We present a new method EmbeddingCAM, which generalizes the Grad-CAM for embedding networks. We show the effectiveness of our method on CUB-200-2011 dataset and also present quantitative and qualitative analysis on the dataset.
  arXiv  Detail & Related papers  (2024-02-01T04:58:06Z)
• MoBYv2AL: Self-supervised Active Learning for Image Classification [57.4372176671293]
  We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY, one of the most successful self-supervised learning algorithms, to the AL pipeline. We achieve state-of-the-art results when compared to recent AL methods.
  arXiv  Detail & Related papers  (2023-01-04T10:52:02Z)
• Zero-Shot Temporal Action Detection via Vision-Language Prompting [134.26292288193298]
  We propose a novel zero-Shot Temporal Action detection model via Vision-LanguagE prompting (STALE) Our model significantly outperforms state-of-the-art alternatives. Our model also yields superior results on supervised TAD over recent strong competitors.
  arXiv  Detail & Related papers  (2022-07-17T13:59:46Z)
• Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study [0.13999481573773068]
  convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP) have been proven to be an effective method of image classification. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data. We propose using the method of transfer learning to decrease the training time and reduce the dependence on large labeled dataset.
  arXiv  Detail & Related papers  (2022-06-19T14:36:33Z)
• F-CAM: Full Resolution CAM via Guided Parametric Upscaling [20.609010268320013]
  Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. CAM methods are typically integrated within off-the-shelf CNN backbones, such as ResNet50. We introduce a generic method for parametric upscaling of CAMs that allows constructing accurate full resolution CAMs.
  arXiv  Detail & Related papers  (2021-09-15T04:45:20Z)
• Calibrating Class Activation Maps for Long-Tailed Visual Recognition [60.77124328049557]
  We present two effective modifications of CNNs to improve network learning from long-tailed distribution. First, we present a Class Activation Map (CAMC) module to improve the learning and prediction of network classifiers. Second, we investigate the use of normalized classifiers for representation learning in long-tailed problems.
  arXiv  Detail & Related papers  (2021-08-29T05:45:03Z)
• Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [89.56292219019163]
  Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations. We illustrate a previously unrecognized limitation of the popular neural network explanation method Grad-CAM. We propose HiResCAM, a class-specific explanation method that is guaranteed to highlight only the locations the model used to make each prediction.
  arXiv  Detail & Related papers  (2020-11-17T19:26:14Z)
• Eigen-CAM: Class Activation Map using Principal Components [1.2691047660244335]
  This paper builds on previous ideas to cope with the increasing demand for interpretable, robust, and transparent models. The proposed Eigen-CAM computes and visualizes the principle components of the learned features/representations from the convolutional layers.
  arXiv  Detail & Related papers  (2020-08-01T17:14:13Z)
• Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer [116.46533207849619]
  We study the impact of scale and location mismatch in the few-shot learning scenario. We propose a novel Spatially-aware Matching scheme to effectively perform matching across multiple scales and locations.
  arXiv  Detail & Related papers  (2020-01-06T14:10:20Z)

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.