# DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration

arXiv: https://arxiv.org/abs/2405.18882 · HTML: https://mlsanity.com/paper/2405.18882/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 29 May 2024.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Densely Nested Top-Down Flows for Salient Object Detection](https://mlsanity.com/paper/2102.09133/) (2021)
- [Efficient Image Captioning for Edge Devices](https://mlsanity.com/paper/2212.08985/) (2022)
- [Deeply Explain CNN via Hierarchical Decomposition](https://mlsanity.com/paper/2201.09205/) (2022)

## Built on by (most cited first, 2 of 2)

- [Region-CAM: Towards Accurate Object Regions in Class Activation Maps for Weakly Supervised Learning Tasks](https://mlsanity.com/paper/2510.25134/) (2025)
- [Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization](https://mlsanity.com/paper/2603.25083/) (2026)

## Abstract (arXiv)

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.