arXiv Computer Vision

FogTTA: Online Test-Time Adaptation for Robust Transformer-based Object Detection in Foggy Weather

FogTTA is an online test-time adaptation framework that enhances Transformer-based object detectors, specifically RF-DETR, for foggy weather conditions. It operates without source data or retraining by using a teacher-student architecture: the deployed model generates pseudo labels from weakly augmented inputs, which are refined and used to train a student model on strongly augmented samples with Varifocal loss. The teacher model is updated via exponential moving averaging, enabling real-time, stable adaptation and improved detection accuracy in foggy environments.

arXiv Computer Vision

Sep 23

Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation

The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).

By Sepideh Gohari, Goodarz Mehr, Azim Eskandarian

agents robotics fine-tuning benchmarks

Hugging Face Trending Papers

Sep 22

Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation

Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...

agents robotics fine-tuning benchmarks

Hugging Face Trending Papers

Jun 8

ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity

3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.

computer-vision efficiency multimodal benchmarks

arXiv Computer Vision

Oct 2

Weather-Aware Domain Adaptation for Street-View Weather Recognition

arXiv:2610.02000v1 Announce Type: new Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...

By Hossein Maghsoumi, George Atia, Yaser P. Fallah

agents robotics fine-tuning benchmarks safety

arXiv AI

Jun 9

ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity

arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.

By Debojyoti Biswas, Xianbiao Hu

computer-vision efficiency multimodal benchmarks

arXiv Computer Vision

Sep 22

Towards robust multimodal 3D object detection via visual foundation models

arXiv:2609.23541v1 Announce Type: new Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...

By Ziying Song, Lin Liu, Hongyu Pan, Shaoqing Xu, Lei Yang, Mingzhe Guo, Caiyan Jia

agents computer-vision robotics multimodal benchmarks

arXiv Machine Learning

Jul 8

A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog

arXiv:2607. 05467v1 Announce Type: cross Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream detection and tracking.

By Amir Pouladi, Vesal Ahsani, Haijun Li, Homayoun Najjaran, Afzal Suleman

llms computer-vision

arXiv AI

Jul 28

ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness

arXiv:2607. 23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality.

By Qiao Yan, Yihan Wang, Zhenghao Xing, Jiaqi Xu, Pheng-Ann Heng

llms agents reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 2

Evaluating the Robustness of Anti-UAV Detection under Controlled Fog Degradation: Fog-Aware Training and Clear-Sky Tradeoff

arXiv:2610.00141v1 Announce Type: new Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...

By Gur Levy Birkental, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag

benchmarks

arXiv Computer Vision

Sep 1

Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation

arXiv:2409.11018v3 Announce Type: replace Abstract: The LiDAR 3D object detector that balances accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many ex...

By Rui Yu, Runkai Zhao, Jiagen Li, Qingsong Zhao, HuaiCheng Yan, Meng Wang

llms agents computer-vision robotics efficiency safety

arXiv AI

Aug 26

Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection

The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.

By Long Hoang Pham, Quoc Pham-Nam Ho, Huy-Hung Nguyen, Duong Nguyen-Ngoc Tran, Ngoc Doan-Minh Huynh, Cu Quoc Le, Hoang-Khang Nguyen, Hyung-Min Jeon, Chi Dai Tran, Son Hong Phan, Duong Khac Vu, Trinh Le Ba Khanh, Jae Wook Jeon

llms computer-vision fine-tuning efficiency benchmarks safety

arXiv Computer Vision

Oct 7

Mitigating Illumination-Induced Domain Shift in Night-Time Pedestrian Detection for Intelligent Vehicles using Annotation-Preserving Diffusion Augmentation

The paper introduces Contrastive‑SDXL, a day‑to‑night augmentation framework that uses SDXL‑Turbo and Low‑Rank Adaptation to generate realistic night‑time images for pedestrian detection. It employs a patch‑wise semantic contrastive loss guided by a pretrained DINOv2 encoder and an object consistency loss to preserve pedestrian and local semantic structure during translation, achieving a FID of 22.5. Detectors trained on these synthetic images show a 6‑7% reduction in miss rate compared to a daytime‑only baseline, approaching the performance of models trained on real night‑time data.

By Franky George, Muhammad Khalid, Adil Khan, Koorosh Aslansefat

diffusion fine-tuning safety

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