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cs.AI2026
ToPA: Block-wise Toeplitz Adaptation for Expressive and Efficient Fine-Tuning
Sicong Li, Qianqian Xu, Zhiyong Yang +4
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prevalent strategy for adapting large pre-trained models. Among PEFT techniques, reparameterization-based methods, particula…
cs.CV2026
Making Training-Free Diffusion Segmentors Scale with the Generative Power
Benyuan Meng, Qianqian Xu, Zitai Wang +3
As powerful generative models, text-to-image diffusion models have recently been explored for discriminative tasks. A line of research focuses on adapting a pre-trained diffusion m…
cs.CV2026
BlackMirror: Black-Box Backdoor Detection for Text-to-Image Models via Instruction-Response Deviation
Feiran Li, Qianqian Xu, Shilong Bao +4
This paper investigates the challenging task of detecting backdoored text-to-image models under black-box settings and introduces a novel detection framework BlackMirror. Existing…