13 papers
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
Anjie Qiao, Zhen Wang, Yaliang Li +2
Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable v…
VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
Zhiwen Li, Zhongjie Duan, Jinyan Ye +4
Replicating In-Context Learning (ICL) in computer vision remains challenging due to task heterogeneity. We propose \textbf{VIRAL}, a framework that elicits visual reasoning from a…
Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
Jinyan Ye, Zhongjie Duan, Zhiwen Li +4
Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates. However, existing approaches that opt…
AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation
Zhiwen Li, Zhongjie Duan, Die Chen +4
Despite recent advances in photorealistic image generation through large-scale models like FLUX and Stable Diffusion v3, the practical deployment of these architectures remains con…
AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models
Die Chen, Zhongjie Duan, Zhiwen Li +4
Diffusion models have recently become the dominant paradigm for image generation, yet existing systems struggle to interpret and follow numeric instructions for adjusting semantic…
ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions
Yule Li, Yifeng Lu, Zhen Wang +3
In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., ac…
Responsible Diffusion Models via Constraining Text Embeddings within Safe Regions
Zhiwen Li, Die Chen, Mingyuan Fan +4
The remarkable ability of diffusion models to generate high-fidelity images has led to their widespread adoption. However, concerns have also arisen regarding their potential to pr…
Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models
Die Chen, Zhiwen Li, Cen Chen +5
Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabiliti…
Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion Models
Die Chen, Zhiwen Li, Cen Chen +5
Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabiliti…
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction
Zhongjie Duan, Qianyi Zhao, Cen Chen +4
The emergence of diffusion models has significantly advanced image synthesis. The recent studies of model interaction and self-corrective reasoning approach in large language model…
Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models
Die Chen, Zhiwen Li, Mingyuan Fan +4
Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which lead…
ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning
Zhongjie Duan, Wenmeng Zhou, Cen Chen +2
Recently, advancements in video synthesis have attracted significant attention. Video synthesis models such as AnimateDiff and Stable Video Diffusion have demonstrated the practica…
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness
Mingyuan Fan, Xiaodan Li, Cen Chen +2
A prevailing belief in attack and defense community is that the higher flatness of adversarial examples enables their better cross-model transferability, leading to a growing inter…