  Interpreting complex deep networks, notably pre-trained vision-language
models (VLMs), is a formidable challenge. Current Class Activation Map (CAM)
methods highlight regions revealing the model's decision-making basis but lack
clear saliency maps and detailed interpretability. To bridge this gap, we
propose DecomCAM, a novel decomposition-and-integration method that distills
shared patterns from channel activation maps. Utilizing singular value
decomposition, DecomCAM decomposes class-discriminative activation maps into
orthogonal sub-saliency maps (OSSMs), which are then integrated together based
on their contribution to the target concept. Extensive experiments on six
benchmarks reveal that DecomCAM not only excels in locating accuracy but also
achieves an optimizing balance between interpretability and computational
efficiency. Further analysis unveils that OSSMs correlate with discernible
object components, facilitating a granular understanding of the model's
reasoning. This positions DecomCAM as a potential tool for fine-grained
interpretation of advanced deep learning models. The code is avaible at
https://github.com/CapricornGuang/DecomCAM.
