20 citations · 67 across the 10 of their papers we have counts for
10 papers
Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices
Bojia Zi, Xianbiao Qi, Lingzhi Wang +3
In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs). In contrast to LoRA and other low-rank adaptation met…
detrex: Benchmarking Detection Transformers
Tianhe Ren, Shilong Liu, Feng Li +13
The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation
Yukun Huang, Jianan Wang, Yukai Shi +3
Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled 3D content creation by optimizing a randomly initialized differentiable 3D represen…
Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant
Xianbiao Qi, Jianan Wang, Lei Zhang
This article provides a comprehensive understanding of optimization in deep learning, with a primary focus on the challenges of gradient vanishing and gradient exploding, which nor…
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars
Yukun Huang, Jianan Wang, Ailing Zeng +5
We present DreamWaltz, a novel framework for generating and animating complex 3D avatars given text guidance and parametric human body prior. While recent methods have shown encour…
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
Xianbiao Qi, Jianan Wang, Yihao Chen +2
We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previ…
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
Yihao Chen, Xianbiao Qi, Jianan Wang +1
We propose DisCo-CLIP, a distributed memory-efficient CLIP training approach, to reduce the memory consumption of contrastive loss when training contrastive learning models. Our ap…
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation
Xuan Ju, Ailing Zeng, Chenchen Zhao +3
Controllable human image generation (HIG) has numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable bra…
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
Xuan Ju, Ailing Zeng, Jianan Wang +2
Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention o…
Entity-Level Text-Guided Image Manipulation
Yikai Wang, Jianan Wang, Guansong Lu +4
Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical appl…