# Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection

arXiv: https://arxiv.org/abs/2007.06227 · HTML: https://mlsanity.com/paper/2007.06227/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 30 of 72)

- [Visual Saliency Transformer](https://mlsanity.com/paper/2104.12099/) (2021)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning](https://mlsanity.com/paper/2311.15011/) (2023)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [RGB-D Saliency Detection via Cascaded Mutual Information Minimization](https://mlsanity.com/paper/2109.07246/) (2021)
- [CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection](https://mlsanity.com/paper/2112.02363/) (2021)
- [TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network](https://mlsanity.com/paper/2108.03990/) (2021)
- [Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks](https://mlsanity.com/paper/2303.15710/) (2023)
- [Lesion-aware Dynamic Kernel for Polyp Segmentation](https://mlsanity.com/paper/2301.04904/) (2023)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.01971/) (2021)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2107.01779/) (2021)
- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2109.05676/) (2021)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection](https://mlsanity.com/paper/2110.00338/) (2021)
- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection](https://mlsanity.com/paper/2406.01127/) (2024)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (2021)
- [Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2108.05076/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [Spider: A Unified Framework for Context-dependent Concept Segmentation](https://mlsanity.com/paper/2405.01002/) (2024)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection](https://mlsanity.com/paper/2104.01784/) (2021)
- [S3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving](https://mlsanity.com/paper/2401.11414/) (2024)
- [Open-Vocabulary Camouflaged Object Segmentation](https://mlsanity.com/paper/2311.11241/) (2023)
- [Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction](https://mlsanity.com/paper/2203.04895/) (2022)

## Abstract (arXiv)

The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate dynamic filters with receptive fields of different sizes. In the end, we implement a kind of more flexible and efficient multi-scale cross-modal feature processing, i.e. dynamic dilated pyramid module. In order to make the predictions have sharper edges and consistent saliency regions, we design a hybrid enhanced loss function to further optimize the results. This loss function is also validated to be effective in the single-modal RGB SOD task. In terms of six metrics, the proposed method outperforms the existing twelve methods on eight challenging benchmark datasets. A large number of experiments verify the effectiveness of the proposed module and loss function. Our code, model and results are available at urlhttps://github.com/lartpang/HDFNet.