StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.GR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CrowdVLA introduces vision-language-action agents for crowd simulation that reason about scene semantics, social norms, and action consequences using fine-tuned models and simulation rollouts.
citing papers explorer
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StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.
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CrowdVLA: Embodied Vision-Language-Action Agents for Context-Aware Crowd Simulation
CrowdVLA introduces vision-language-action agents for crowd simulation that reason about scene semantics, social norms, and action consequences using fine-tuned models and simulation rollouts.