84 citations · 375 across the 68 of their papers we have counts for
14 papers · 1 filter
ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer
Zhen Han, Zeyinzi Jiang, Yulin Pan +5
Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primari…
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Lijie Hu, Chenyang Ren, Huanyi Xie +5
Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled…
ColorEdit: Training-free Image-Guided Color editing with diffusion model
Xingxi Yin, Zhi Li, Jingfeng Zhang +2
Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to a…
An Individual Identity-Driven Framework for Animal Re-Identification
Yihao Wu, Di Zhao, Jingfeng Zhang +1
Reliable re-identification of individuals within large wildlife populations is crucial for biological studies, ecological research, and wildlife conservation. Classic computer visi…
Towards Multi-dimensional Explanation Alignment for Medical Classification
Lijie Hu, Songning Lai, Wenshuo Chen +5
The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several…
ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning
Zhongsheng Wang, Jiamou Liu, Qiming Bao +2
Large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated impressive capabilities in various generative tasks. However, their performance is often hampered by limita…
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
Yukai Xu, Jingfeng Zhang, Yujie Gu
In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level ch…
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
Jiacheng Zhang, Feng Liu, Dawei Zhou +2
Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations ar…
Text Guided Image Editing with Automatic Concept Locating and Forgetting
Jia Li, Lijie Hu, Zhixian He +3
With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly i…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…
StyleBooth: Image Style Editing with Multimodal Instruction
Zhen Han, Chaojie Mao, Zeyinzi Jiang +2
Given an original image, image editing aims to generate an image that align with the provided instruction. The challenges are to accept multimodal inputs as instructions and a scar…
Locate, Assign, Refine: Taming Customized Promptable Image Inpainting
Yulin Pan, Chaojie Mao, Zeyinzi Jiang +3
Prior studies have made significant progress in image inpainting guided by either text description or subject image. However, the research on inpainting with flexible guidance or c…
Make Me Happier: Evoking Emotions Through Image Diffusion Models
Qing Lin, Jingfeng Zhang, Yew-Soon Ong +1
Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, mak…
Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models
Zihao Luo, Xilie Xu, Feng Liu +3
Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss.…