Xin Sun

UFFM achieves superior pseudo-label quality by harmonizing labeled and unlabeled data training, challenging the dominance of manual labeling in semi-supervised learning.

DAC-Pose achieves remarkable fidelity in human generation, maintaining texture and identity consistency even under extreme viewpoint changes.

GAFIC achieves unprecedented accuracy in image cropping by fusing local and global features, outperforming traditional methods that compromise pixel integrity.

Despite high rationale identification, search-augmented models struggle with refusal, achieving only 42.9% correct halting on unanswerable multi-hop questions.

EviSD achieves state-of-the-art performance in question-answering tasks by leveraging privileged evidence, outperforming existing methods while maintaining efficiency in response generation.

Later models may resolve more coding tasks, but they don't necessarily produce better quality patches in terms of performance metrics.