arXiv subjects

Manfredo Manfredini

Publications and source records attributed to Manfredo Manfredini.

2 recordsLinked to original sources

arXiv2026

MMS-VPR: A Fine-Grained Multimodal Street-Level Visual Place Recognition Dataset and Evaluation Benchmark for Dense Pedestrian Environments

Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and underrepresent dense pedestrian street scenes, particularly in non-Western urban contexts. We present MMS-VPR, a challenging fine-grained multimodal benchmark for pedestrian-level place recognition in dense urban environments. MMS-VPR comprises 110,529 images and 2,527 video clips across 208 fine-grained location classes within a $\sim$70,800 m$^2$ urban district in Chengdu, China, with adjacent classes separated by as little as 10-20 m. Unlike large-area VPR datasets, MMS-VPR elevates difficulty through visual ambiguity: repetitive storefronts, dense occlusion, and rapid viewpoint changes. Field data were collected in 2024 following a systematic multi-direction, dual-perspective protocol; social media imagery spans seven years (2019-2025), providing long-term appearance change across 31,726 geolocated photographs. Each location features day-night coverage, multiple viewing angles, and multimodal annotations including GPS coordinates, timestamps, semantic text, and an explicit spatial graph with space syntax enrichments. We release MMS-VPRlib, a unified benchmarking platform consolidating 22 baselines spanning shallow ML, CNN, Transformer, graph neural network (GNN), and multimodal architectures under a standardized, reproducible pipeline. Controlled modality ablations show that video temporal features are the most powerful single modality (+15.8% accuracy over image-only), semantic text provides superior coarse retrieval (R@5 = 0.988 alone), and full multimodal fusion achieves 0.981 accuracy, a +19.2% gain over image-only, while graph-structured modeling provides additional signal in fine-grained structural subsets. The dataset is available at https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR and the benchmark at https://github.com/yiasun/MMS-VPRlib.

cs.CV↗

arXiv2026

Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception

We present Urban-ImageNet, a large-scale multi-modal dataset and evaluation benchmark for urban space perception from user-generated social media imagery. The corpus contains over 2 Million public social media images and paired textual posts collected from Weibo across 61 urban sites in 24 Chinese cities across 2019-2025, with controlled benchmark subsets at 1K, 10K, and 100K scale and a full 2M corpus for large-scale training and evaluation. Urban-ImageNet is organized by HUSIC, a Hierarchical Urban Space Image Classification framework that defines a 10-class taxonomy grounded in urban theory. The taxonomy is designed to distinguish activated and non-activated public spaces, exterior and interior urban environments, accommodation spaces, consumption content, portraits, and non-spatial social-media content. Rather than treating urban imagery as generic scene data, Urban-ImageNet evaluates whether machine perception models can capture spatial, social, and functional distinctions that are central to urban studies. The benchmark supports three tasks within one standardized library: (T1) urban scene semantic classification, (T2) cross-modal image-text retrieval, and (T3) instance segmentation. Our experiments evaluate representative vision, vision-language, and segmentation models, revealing strong performance on supervised scene classification but more challenging behavior in cross-modal retrieval and instance-level urban object segmentation. A multi-scale study further examines how model performance changes as balanced training data increases from 1K, 10K to 100K images. Urban-ImageNet provides a unified, theory-grounded, multi-city benchmark for evaluating how AI systems perceive and interpret contemporary urban spaces across modalities, scales, and task formulations. Dataset and benchmark are available at: huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet and github.com/yiasun/dataset-2.

cs.CV↗