Jingyuan Zhang

KeepAD achieves a 7.9× speedup in zero-shot anomaly detection while preserving crucial defect information, challenging the trade-off between efficiency and accuracy.

ASUMOT achieves robust UAV tracking by leveraging motion consistency, reducing identity confusion in fragmented event data.

Local-Preserving Supervised Fine-Tuning can enhance model performance without sacrificing the rich diversity of pretrained knowledge, achieving superior results in both accuracy and diversity metrics.

Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.