arXiv Computer Vision

RGB-to-IR image translation for infrared vehicle detection in unseen UAV domains

The paper explores using generative models to translate RGB UAV images into synthetic infrared (IR) images for training vehicle detectors in domains where real IR data is scarce. Various translators—supervised GANs, ControlNet-based diffusion models, and LoRA-ed foundation models—were trained on paired RGB-IR datasets and applied to unseen target datasets to generate synthetic IR data. The synthetic IR images, especially those produced by Stable Diffusion 3.5 with ControlNet, significantly improved detection performance on unseen IR test sets, outperforming RGB and grayscale baselines and narrowing the gap to real IR data.

arXiv AI

Aug 19

Training with synthetic data for drone detection in thermal imagery

The paper explores a synthetic-first training approach for detecting drones in medium- and long-wave infrared imagery, combining synthetic scene generation with fine-tuning on real data. It demonstrates that synthetic data can establish initial object representations, but real infrared data is crucial to close domain gaps and improve reliability. The study finds that aligning datasets has a greater impact on performance than increasing model size, and that semantic alignment in feature space is the strongest predictor of success, with radiometric factors like entropy and dynamic range also contributing.

By Tanel Liiv, Sander Soodla, Nzamba Bignoumba, Alma M. Liezenga, Toomas Pruuden

robotics fine-tuning safety

arXiv AI

Oct 7

What Frame-Level Labels Can and Cannot Do for Small-UAV Point Detection in Thermal Video

arXiv:2610.07705v1 Announce Type: cross Abstract: The growing use of unmanned aerial vehicles (UAVs) has increased the importance of image-based UAV detection. Learning-based detectors are trained on...

By Wonbin Son, Gyumum Choi, Junil Seo, Hyungjoon Kim

arXiv AI

Jul 7

Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models

arXiv:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.

By Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar, Ahish Deshpande, Celso M de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre

llms multimodal

arXiv Computer Vision

Sep 3

Domain shift-robust object detection with GenAI image editing

The paper investigates using diffusion-based generative image editing to improve object detector robustness against domain shifts, specifically camouflaged military vehicle detection. By synthetically adding foliage, netting, and multi‑spectral camouflage to training data with models such as Qwen Image Edit 2509 and Flux.2 Dev, the authors demonstrate significant mAP gains (up to +20.1 for foliage) over detectors trained on uncamouflaged data. LoRA fine‑tuning further boosts performance for the more challenging multi‑spectral camouflage.

By Isabel D. Stein, Thijs A. Eker, Sebastiaan P. Snel, Ella P. Fokkinga, Klamer Schutte, Luca Ambrogioni, Friso G. Heslinga

diffusion computer-vision fine-tuning

arXiv Machine Learning

Aug 24

On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift

arXiv:2608.21254v1 Announce Type: cross Abstract: Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reduci...

By Nikhilesh Prabhakar, Pranuthi Tenali, Wilfredo Abudeye Fernandez, Shekhar Borah, Athresh Karanam, Erik Blasch, Prabha Sundaravadivel, Sriraam Natarajan

computer-vision fine-tuning

arXiv AI

Sep 3

InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models

InfraPatch is a white‑box, per‑instance framework that generates small grayscale patches to target infrared‑adapted vision‑language models (IR‑VLMs). The method optimizes a single‑channel patch within a 5% local‑area budget, using proxy‑guided placement and task‑adaptive objectives to induce desired behaviors in image classification, captioning, and binary visual question answering. Across ten IR‑VLM variants tested on synthetic infrared images, InfraPatch achieves targeted attack success rates ranging from 86% to 100%, revealing significant vulnerability differences among architectures and tasks.

By Chengyin Hu, Dingyi Lu, Jiaju Han, Xiang Chen, Weiwen Shi, Jiahuan Long, Yiwei Wei, Jiujiang Guo

llms computer-vision nlp multimodal safety

arXiv Computer Vision

Sep 2

Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3

The paper examines RGB‑infrared fusion for binary wildfire segmentation using UAV imagery on the FLAME3 dataset. It compares RGB and infrared baselines with three fusion strategies across U‑Net, DeepLabV3+, and SegFormer architectures. Results show thermal data dominates segmentation performance, and feature‑level fusion with transformer‑based models yields the best results.

By Matheus F. Kovaleski, Lu\'is Garrote, Cristiano Premebida, J\'er\^ome Mendes, Jo\~ao Ruivo Paulo

llms computer-vision multimodal

arXiv AI

Jul 15

Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation

arXiv:2607. 12065v1 Announce Type: cross Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions.

By Robel Mamo, Rajitha de Silva, Grzegorz Cielniak, Taeyeong Choi

agents computer-vision robotics multimodal benchmarks

arXiv AI

Jul 8

AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models

arXiv:2607. 06485v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial robustness remains unexamined.

By Cong Su, Jiaju Han, Xuemeng Sun, Chengyin Hu, Qike Zhang, Jiujiang Guo, Yiwei Wei, Jiahuan Long

llms multimodal benchmarks safety data-engineering

arXiv AI

Jul 21

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification

arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.

By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 12

Augmentation techniques for video surveillance in the visible and thermal spectral range

arXiv:2606. 13042v1 Announce Type: new Abstract: In intelligent video surveillance, cameras record image sequences during day and night.

By Vanessa Buhrmester, Ann-Kristin Grosselfinger, David Munch, Michael Arens

computer-vision

arXiv AI

Jun 16

FusionRS: A Large-Scale RGB-Infrared Remote Sensing Dataset for Dual-Modal Vision-Language Foundation Models

arXiv:2606. 17020v1 Announce Type: cross Abstract: Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored.

By Jiaju Han, Ben Zhang, Xuemeng Sun, Qike Zhang, Yuxian Dong, Chengyin Hu, Fengyu Zhang, Yiwei Wei, Jiujiang Guo

llms fine-tuning multimodal benchmarks safety

Related stories