# Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection

arXiv: https://arxiv.org/abs/2406.01127 · HTML: https://mlsanity.com/paper/2406.01127/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 87 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Visual Saliency Transformer](https://mlsanity.com/paper/2104.12099/) (2021)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [RGBT Salient Object Detection: A Large-scale Dataset and Benchmark](https://mlsanity.com/paper/2007.03262/) (2020)
- [SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection](https://mlsanity.com/paper/2204.05585/) (2022)
- [RGB-T Image Saliency Detection via Collaborative Graph Learning](https://mlsanity.com/paper/1905.06741/) (2019)
- [Uncertainty Inspired RGB-D Saliency Detection](https://mlsanity.com/paper/2009.03075/) (2020)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [Accurate RGB-D Salient Object Detection via Collaborative Learning](https://mlsanity.com/paper/2007.11782/) (2020)
- [A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach](https://mlsanity.com/paper/1701.02829/) (2017)
- [CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection](https://mlsanity.com/paper/2112.02363/) (2021)
- [RGBD Salient Object Detection via Deep Fusion](https://mlsanity.com/paper/1607.03333/) (2016)
- [Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection](https://mlsanity.com/paper/2005.02315/) (2020)
- [Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection](https://mlsanity.com/paper/2010.05537/) (2020)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)

## Built on by (most cited first, 19 of 19)

- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation](https://mlsanity.com/paper/2410.11586/) (2024)
- [WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2508.12250/) (2025)
- [HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection](https://mlsanity.com/paper/2606.31496/) (2026)
- [SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection](https://mlsanity.com/paper/2510.03689/) (2025)
- [Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios](https://mlsanity.com/paper/2601.00537/) (2026)
- [Deep Fourier-embedded Network for RGB and Thermal Salient Object Detection](https://mlsanity.com/paper/2411.18409/) (2024)
- [HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection](https://mlsanity.com/paper/2509.18738/) (2025)
- [STENet: Superpixel Token Enhancing Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2603.21999/) (2026)
- [RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation](https://mlsanity.com/paper/2609.12622/) (2026)
- [Efficient Fourier Filtering Network with Contrastive Learning for AAV-based Unaligned Bimodal Salient Object Detection](https://mlsanity.com/paper/2411.03728/) (2024)
- [SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images](https://mlsanity.com/paper/2503.02270/) (2025)
- [Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach](https://mlsanity.com/paper/2509.15573/) (2025)
- [RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detection](https://mlsanity.com/paper/2603.12685/) (2026)
- [Weakly-Supervised RGB-D Salient Object Detection via SAM-driven Pseudo Annotation and State Space Interaction-based Diffusion](https://mlsanity.com/paper/2607.15041/) (2026)
- [Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2607.21032/) (2026)
- [VCP-DCN: Beyond Visual Concealed Property via Depth Collaborative Network for Camouflaged Object Detection](https://mlsanity.com/paper/2607.27843/) (2026)
- [S3AM: A Single-Stream SAM with Reliability-Calibrated Frequency Adapter for Multi-modal Salient Object Detection](https://mlsanity.com/paper/2608.17475/) (2026)
- [S2A:Semantic-to-Spatial Alignment for Alignment-Free RGB-T Salient Object Detection](https://mlsanity.com/paper/2609.27413/) (2026)

## Abstract (arXiv)

Multi-modal salient object detection (MSOD) aims to boost saliency detection performance by integrating visible sources with depth or thermal infrared ones. Existing methods generally design different fusion schemes to handle certain issues or challenges. Although these fusion schemes are effective at addressing specific issues or challenges, they may struggle to handle multiple complex challenges simultaneously. To solve this problem, we propose a novel adaptive fusion bank that makes full use of the complementary benefits from a set of basic fusion schemes to handle different challenges simultaneously for robust MSOD. We focus on handling five major challenges in MSOD, namely center bias, scale variation, image clutter, low illumination, and thermal crossover or depth ambiguity. The fusion bank proposed consists of five representative fusion schemes, which are specifically designed based on the characteristics of each challenge, respectively. The bank is scalable, and more fusion schemes could be incorporated into the bank for more challenges. To adaptively select the appropriate fusion scheme for multi-modal input, we introduce an adaptive ensemble module that forms the adaptive fusion bank, which is embedded into hierarchical layers for sufficient fusion of different source data. Moreover, we design an indirect interactive guidance module to accurately detect salient hollow objects via the skip integration of high-level semantic information and low-level spatial details. Extensive experiments on three RGBT datasets and seven RGBD datasets demonstrate that the proposed method achieves the outstanding performance compared to the state-of-the-art methods. The code and results are available at https://github.com/Angknpng/LAFB.