ServImage supplies a commercial-design benchmark, three-dimensional scoring rubric, and 82%-accurate payment predictor trained on 33k human-annotated images from paid projects.
arXiv preprint
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
FAST uses a Temporal-Spatial-Temporal structure with attention and Mamba modules plus learnable embeddings to achieve better accuracy on traffic prediction tasks than previous models.
citing papers explorer
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ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services
ServImage supplies a commercial-design benchmark, three-dimensional scoring rubric, and 82%-accurate payment predictor trained on 33k human-annotated images from paid projects.
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FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
FAST uses a Temporal-Spatial-Temporal structure with attention and Mamba modules plus learnable embeddings to achieve better accuracy on traffic prediction tasks than previous models.