Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

1 code implementation • 10 Nov 2023 • Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf

We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).

Controlling Text-to-Image Diffusion by Orthogonal Finetuning

no code implementations • NeurIPS 2023 • Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf

To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks.

Iterative Teaching by Data Hallucination

1 code implementation • 31 Oct 2022 • Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i. e., a pool of finite samples), which greatly limits the teacher's capability.

Hallucination

3D Multi-Object Tracking with Differentiable Pose Estimation

no code implementations • 28 Jun 2022 • Dominik Schmauser, Zeju Qiu, Norman Müller, Matthias Nießner

We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments.

3D Multi-Object Tracking Object +1