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.
arXiv:2605.22455v2 Announce Type: replace-cross Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...
The paper introduces XTrack, a unified framework that repurposes a pretrained video Diffusion Transformer (DiT) for cross-view temporal object correspondence. XTrack incorporates focal cross-view attention alignment, hard negative conditioning, and local context cropping to improve grounding across views and time, and it uses a pseudo‑label curation pipeline to generate dense supervision from sparse Ego‑Exo4D data. The authors demonstrate that XTrack achieves robust temporal consistency and identity preservation in cross‑view tracking tasks.
TriDifNet is a diffusion-based network that jointly performs registration, fusion, and detection for misaligned RGB-T pedestrian perception. It includes three modules—RegDM for aligning infrared to visible images, FusDM for generating a detection-oriented fused representation, and DetDM for iterative bounding‑box denoising. The system uses closed‑loop optimization and a Nash‑MTL refinement to balance the three tasks, achieving state‑of‑the‑art detection metrics on the KAIST multispectral benchmark.
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...
arXiv:2609.09881v1 Announce Type: new Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
arXiv:2602. 07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications.
arXiv:2607. 12065v1 Announce Type: cross Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions.
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...
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.
arXiv:2505.12254v3 Announce Type: replace-cross Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...
arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.
Semantically-Guided Domain Randomization (S‑GDR) is an annotation‑free pipeline that uses vision‑language model captioning of a small real reference set, diffusion‑based background synthesis, and mask‑based object composition to generate synthetic training data. In a high‑mix, low‑volume automotive detection benchmark, S‑GDR achieves a mAP50‑95 of 0.739 with only 200 synthetic images, outperforming a domain‑randomized render baseline and several other synthetic data methods under the same budget. These results suggest S‑GDR is a viable alternative for training visual perception systems when annotation, energy, and time resources are severely limited.