# Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

arXiv: https://arxiv.org/abs/1905.08789 · HTML: https://mlsanity.com/paper/1905.08789/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 30 of 38)

- [ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection](https://mlsanity.com/paper/2308.07504/) (2023)
- [Multimodal Object Detection via Probabilistic Ensembling](https://mlsanity.com/paper/2104.02904/) (2021)
- [C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection](https://mlsanity.com/paper/2306.16175/) (2023)
- [XoFTR: Cross-modal Feature Matching Transformer](https://mlsanity.com/paper/2404.09692/) (2024)
- [Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels](https://mlsanity.com/paper/2301.12689/) (2023)
- [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021)
- [Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning](https://mlsanity.com/paper/2110.03143/) (2021)
- [SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.03150/) (2021)
- [PID: Physics-Informed Diffusion Model for Infrared Image Generation](https://mlsanity.com/paper/2407.09299/) (2024)
- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage](https://mlsanity.com/paper/2308.07151/) (2023)
- [Self-training Guided Adversarial Domain Adaptation For Thermal Imagery](https://mlsanity.com/paper/2106.07165/) (2021)
- [Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection](https://mlsanity.com/paper/2405.16038/) (2024)
- [Integrated Multiscale Domain Adaptive YOLO](https://mlsanity.com/paper/2202.03527/) (2022)
- [Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey](https://mlsanity.com/paper/2401.07801/) (2024)
- [Robust pedestrian detection in thermal imagery using synthesized images](https://mlsanity.com/paper/2102.02005/) (2021)
- [Domain Adaptive Person Search](https://mlsanity.com/paper/2207.11898/) (2022)
- [GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection](https://mlsanity.com/paper/2102.12319/) (2021)
- [Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery](https://mlsanity.com/paper/2205.08002/) (2022)
- [Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection](https://mlsanity.com/paper/2507.14643/) (2025)
- [C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection](https://mlsanity.com/paper/2509.23316/) (2025)
- [Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection](https://mlsanity.com/paper/2206.03943/) (2022)
- [Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks](https://mlsanity.com/paper/2411.18288/) (2024)
- [SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems](https://mlsanity.com/paper/2504.15728/) (2025)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery](https://mlsanity.com/paper/2304.07031/) (2023)
- [DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR](https://mlsanity.com/paper/2604.03685/) (2026)
- [Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection](https://mlsanity.com/paper/2103.13970/) (2021)
- [Partially fake it till you make it: mixing real and fake thermal images for improved object detection](https://mlsanity.com/paper/2106.13603/) (2021)
- [Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection](https://mlsanity.com/paper/2309.16592/) (2023)

## Abstract (arXiv)

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach. Our code and pre-trained models are made available at https://github.com/tdchaitanya/MMTOD