CoDA is a lightweight detector using a Noise-Quantization Probe on color non-uniformity that reports strong cross-domain results on the new FakeForm benchmark and competitive cross-model performance on standard tests.
Denoising diffusion probabilistic models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Transformer models with person ID embeddings generate plausible reactive motions from paired boxing interaction data, with the simple Transformer outperforming iTransformer and Crossformer in stability and avoiding posture collapse.
citing papers explorer
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CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection
CoDA is a lightweight detector using a Noise-Quantization Probe on color non-uniformity that reports strong cross-domain results on the new FakeForm benchmark and competitive cross-model performance on standard tests.
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Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models
Transformer models with person ID embeddings generate plausible reactive motions from paired boxing interaction data, with the simple Transformer outperforming iTransformer and Crossformer in stability and avoiding posture collapse.