Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traffic monitoring, and forensic investigation. However, models trained under controlled conditions often degrade in real surveillance scenarios due to changes in viewpoint,...
S. Silva, Otavio T. Remer, G. E. Lima et al.· 0 citations
Vehicle attribute recognition is an important task in intelligent transportation systems, particularly when Automatic License Plate Recognition (ALPR) is unavailable or unreliable. Although vision foundation models have shown strong transferability across domains, their effectiveness for fine-grained vehicle classifica...
Alexandre V. Delazeri, G. E. Lima, E. Nascimento et al.· 0 citations
The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalization power that leverages the immense pre-training knowledge, both foundation models for image and text as well as vision-text hybrids lack the representational power neede...
L. Wojcik, G. E. Lima, S. Silva et al.· 0 citations
UFPR-PEs provides a reproducible and demographically grounded setting for studying face recognition bias under challenging public video conditions and shows that recognition performance varies substantially with image quality, and that subgroup gaps must be interpreted jointly with visual difficulty rather than in isol...
Alexandre Diano, Bernardo Biesseck, Gabriel Polo et al.· 0 citations
This work shows that identity-conditioned face synthesis can be performed at a substantially lower computational cost by a latent Consistency Model with few iterations, without compromising image quality for large-scale synthetic face generation.
Tiago Kienen Chaves, Bernardo Biesseck, David Menotti· 0 citations
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