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Shanghai Jiaotong University
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Reconstruct a high-fidelity, full-head 3D avatar from a single image in under one second, finally breaking the quality-speed tradeoff.
Stop training all your data the same way: this new method adaptively reweights samples based on how "competitive" they are within a group, leading to better performance and robustness.
MLLMs can achieve state-of-the-art multimodal retrieval by learning to compress information into a handful of "bottleneck" tokens, forcing the model to distill relevant semantics.