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
arXiv:2609.39467v1 Announce Type: new
Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-wo...
By ZiAn Wang, MingZhe Liu, Chaoyi Guo, ChangChun Li, Fangming Gu
The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.
By Yukang Shen, Zhiguo Liu, Yingshu Li, Yan Huang
arXiv:2608.20639v1 Announce Type: new
Abstract: Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view map from multi-view images. Recent MVPD methods adop...
By Taiga Yamane, Satoshi Suzuki, Ryo Masumura, Shota Orihashi, Tomohiro Tanaka, Mana Ihori, Naoki Makishima
arXiv:2606. 22574v2 Announce Type: replace-cross Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables.
By Hooman Tavakoli Ghinani, Tatjana Legler, Martin Ruskowski
arXiv:2606. 06899v1 Announce Type: cross Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and within environments.
By Lizhen Zhu, Charantej Reddy Pochimireddy, James Z Wang, Brad Wyble
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
By Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
arXiv:2609.14383v1 Announce Type: new
Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion we...
By Sam Williams, Yuan Xiang
arXiv:2606. 18566v1 Announce Type: cross Abstract: Crowd counting is a fundamental task in computer vision.
By Hao-Yuan Ma, Li Zhang, Yushi Qiu, Jie Gao, Yan Zhang, Bangjun Wang
TriSAR-3D is a new trimodal dataset combining high‑resolution LiDAR, RGB, and thermal imagery to capture people in varied indoor and outdoor poses, including lying and partially occluded positions. The authors propose PaintFusion, a lightweight trimodal 3D detection pipeline that fuses PointPainting evidence with an MLP, achieving 65.4 AP@50 and outperforming LiDAR‑only, RGB+LiDAR, and other trimodal baselines while running faster (51.6 ms per frame). Experiments show that removing lying instances from training drastically reduces detection performance, highlighting the importance of dataset diversity for robust 3D person detection in search and rescue scenarios.
By Markus Kramer, Jan Wachenfeld, Oskar von Stryk
Currently, autonomous driving object detection models face significant data scarcity and generalization challenges when navigating complex Chinese rural traffic scenarios. To address these limitations, we propose a novel real-synthetic mixed object detection dataset tailored specifically for Chinese rural roads and systematically evaluate the performance of 13 mainstream detectors under different real-to-synthetic data ratios, thereby providing empirical evidence for model selection and data strategy design in rural autonomous driving scenarios.
arXiv:2610.20501v1 Announce Type: new
Abstract: Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existi...
By Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai