# Robust Deep Multi-modal Learning Based on Gated Information Fusion Network

arXiv: https://arxiv.org/abs/1807.06233 · HTML: https://mlsanity.com/paper/1807.06233/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 18 of 18)

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Defending Multimodal Fusion Models against Single-Source Adversaries](https://mlsanity.com/paper/2206.12714/) (2022)
- [Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2408.01366/) (2024)
- [CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection](https://mlsanity.com/paper/2411.03013/) (2024)
- [3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection](https://mlsanity.com/paper/2211.13529/) (2022)
- [LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection](https://mlsanity.com/paper/2402.11735/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2203.03568/) (2022)
- [Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving](https://mlsanity.com/paper/2307.16121/) (2023)
- [Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization](https://mlsanity.com/paper/2204.02485/) (2022)
- [Informative Data Selection with Uncertainty for Multi-modal Object Detection](https://mlsanity.com/paper/2304.11697/) (2023)
- [Multi-Perspective Anomaly Detection](https://mlsanity.com/paper/2105.09903/) (2021)
- [D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence](https://mlsanity.com/paper/2210.00087/) (2022)
- [EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery (Extended Version)](https://mlsanity.com/paper/2404.17917/) (2024)
- [JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts](https://mlsanity.com/paper/2412.13708/) (2024)
- [PGMEL: Policy Gradient-based Generative Adversarial Network for Multimodal Entity Linking](https://mlsanity.com/paper/2510.02726/) (2025)
- [FusionNet: a frame interpolation network for 4D heart models](https://mlsanity.com/paper/2603.10212/) (2026)
- [LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving](https://mlsanity.com/paper/2603.21987/) (2026)

## Abstract (arXiv)

The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.