MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Datasets and Benchmarks Track

Bibtex Paper Supplemental


REN WANG, Jiayue Wang, Tae Sung Kim, JINSUNG KIM, Hyuk-Jae Lee


Combining information from multiple views is essential for discriminating similar objects. However, existing datasets for multi-view object classification have several limitations, such as synthetic and coarse-grained objects, no validation split for hyperparameter tuning, and a lack of view-level information quantity annotations for analyzing multi-view-based methods. To address this issue, this study proposes a new dataset, MVP-N, which contains 44 retail products, 16k real captured views with human-perceived information quantity annotations, and 9k multi-view sets. The fine-grained categorization of objects naturally generates multi-view label noise owing to the inter-class view similarity, allowing the study of learning from noisy labels in the multi-view case. Moreover, this study benchmarks four multi-view-based feature aggregation methods and twelve soft label methods on MVP-N. Experimental results show that MVP-N will be a valuable resource for facilitating the development of real-world multi-view object classification methods. The dataset and code are publicly available at