Keep CALM and Improve Visual Feature Attribution
- URL: http://arxiv.org/abs/2106.07861v1
- Date: Tue, 15 Jun 2021 03:33:25 GMT
- Title: Keep CALM and Improve Visual Feature Attribution
- Authors: Jae Myung Kim, Junsuk Choe, Zeynep Akata, and Seong Joon Oh
- Abstract summary: The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks.
We improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation.
The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm.
- Score: 42.784665606132
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The class activation mapping, or CAM, has been the cornerstone of feature
attribution methods for multiple vision tasks. Its simplicity and effectiveness
have led to wide applications in the explanation of visual predictions and
weakly-supervised localization tasks. However, CAM has its own shortcomings.
The computation of attribution maps relies on ad-hoc calibration steps that are
not part of the training computational graph, making it difficult for us to
understand the real meaning of the attribution values. In this paper, we
improve CAM by explicitly incorporating a latent variable encoding the location
of the cue for recognition in the formulation, thereby subsuming the
attribution map into the training computational graph. The resulting model,
class activation latent mapping, or CALM, is trained with the
expectation-maximization algorithm. Our experiments show that CALM identifies
discriminative attributes for image classifiers more accurately than CAM and
other visual attribution baselines. CALM also shows performance improvements
over prior arts on the weakly-supervised object localization benchmarks. Our
code is available at https://github.com/naver-ai/calm.
Related papers
- Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition [55.97779732051921]
State-of-the-art classifiers for facial expression recognition (FER) lack interpretability, an important feature for end-users.
A new learning strategy is proposed to explicitly incorporate AU cues into classifier training, allowing to train deep interpretable models.
Our new strategy is generic, and can be applied to any deep CNN- or transformer-based classifier without requiring any architectural change or significant additional training time.
arXiv Detail & Related papers (2024-10-01T10:42:55Z) - DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration [25.299607743268993]
Class Activation Map (CAM) methods highlight regions revealing the model's decision-making basis but lack clear saliency maps and detailed interpretability.
We propose DecomCAM, a novel decomposition-and-integration method that distills shared patterns from channel activation maps.
Experiments reveal that DecomCAM not only excels in locating accuracy but also achieves an optimizing balance between interpretability and computational efficiency.
arXiv Detail & Related papers (2024-05-29T08:40:11Z) - BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale
Weakly Supervised Applications [69.22739434619531]
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.
arXiv Detail & Related papers (2023-09-07T06:45:43Z) - Importance Sampling CAMs for Weakly-Supervised Segmentation [16.86352815414646]
Class activation maps (CAMs) can be used to localize and segment objects in images by means of class activation maps (CAMs)
In this work, we approach both problems with two contributions for improving CAM learning.
We conduct experiments on the PASCAL VOC 2012 benchmark dataset to demonstrate that these modifications significantly increase the performance in terms of contour accuracy.
arXiv Detail & Related papers (2022-03-23T14:54:29Z) - Towards Learning Spatially Discriminative Feature Representations [26.554140976236052]
We propose a novel loss function, termed as CAM-loss, to constrain the embedded feature maps with the class activation maps (CAMs)
CAM-loss drives the backbone to express the features of target category and suppress the features of non-target categories or background.
Experimental results show that CAM-loss is applicable to a variety of network structures and can be combined with mainstream regularization methods to improve the performance of image classification.
arXiv Detail & Related papers (2021-09-03T08:04:17Z) - 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) - 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) - LFI-CAM: Learning Feature Importance for Better Visual Explanation [31.743421292094308]
Class Activation Mapping (CAM) is a powerful technique used to understand the decision making of Convolutional Neural Network (CNN) in computer vision.
We propose a novel architecture, LFI-CAM, which is trainable for image classification and visual explanation in an end-to-end manner.
arXiv Detail & Related papers (2021-05-03T15:12:21Z) - Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
We propose a new self-supervised pretext task, called instance localization.
We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning.
Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
arXiv Detail & Related papers (2021-02-16T17:58:57Z) - 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)
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.