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- The pipeline of EAGLE. - Leveraging the Laplacian matrix, which integrates hierarchically projected image key features and color - affinity, the model exploits eigenvector clustering to capture object-level perspective cues defined - as \( \mathrm{\mathcal{M}}_{eicue} \) and \( \mathrm{\tilde{\mathcal{M}}_{eicue}} \). - Distilling knowledge from \( \mathrm{\mathcal{M}}_{eicue} \), our model further adopts an - object-centric contrastive loss, utilizing the projected vector \( \mathrm{Z} \) and \( \mathrm{\tilde{Z}} \). - The learnable prototype \( \mathrm{\Phi} \) assigned from \( \mathrm{Z} \) and \( \mathrm{\tilde{Z}} \), - acts as a singular anchor that contrasts positive objects and negative objects. Our object-centric - contrastive loss is computed in two distinct manners: intra(\( \mathrm{\mathcal{L}}_{obj} \))- and - inter(\( \mathrm{\mathcal{L}}_{sc} \))-image to ensure semantic consistency. + The pipeline of CXRL. + Our model employs policy gradient optimization utilizing multi-reward feedbacks, fine-tuning image generator and ACE to produce realistic and accurate CXR that corresponds closely to the input report.