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CSNet achieves state-of-the-art image fusion by intelligently contrasting clarity and selecting optimal pixels, redefining how we approach multi-focus image challenges.
CMAM revolutionizes text-based person retrieval by achieving granularity-aware matching that significantly outperforms state-of-the-art methods across diverse query levels.
By harnessing clean latent features, SharpMoE dramatically improves resource allocation to salient tokens, overcoming a key limitation in diffusion MoE architectures.
A novel graph-based dataset pruning method achieves over 40% faster training on ImageNet-1k while maintaining accuracy, challenging the status quo of sample selection.
