Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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,Prompting Future Driven Diffusion Model for Hand Motion Prediction,ns further enhance model performance. Experimental results on the FPHA and HO3D datasets demonstrate that the proposed PromptFDDM achieves state-of-the-art performance in both first- and third-person perspectives.

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,Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics,data. The synthetic images generated by Defect-Gen significantly enhance the performance of defect segmentation models, achieving an improvement in mIoU scores up to . on Defect-Spectrum subsets. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering

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,Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement,ith up to 7.68dB gains on PSNR achieved for six state-of-the-art models. Impressively, our approach maintains efficiency with only 88K extra parameters, setting a new standard in the challenging realm of low-light image enhancement. ..

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,RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation,d local representation and computational efficiency, while incorporating a 4D distance metric that integrates geometric and surface material reflectivity for improved semantic segmentation. To effectively embed high-dimensional RAPiD features, we propose a double-nested autoencoder structure with a