CusConcept: Customized Visual Concept Decomposition with Diffusion Models
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Zhi, Hao, Shaozhe, Han, Kai |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction
by: Hao, Shaozhe, et al.
Published: (2024)
by: Hao, Shaozhe, et al.
Published: (2024)
FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers
by: Zhang, Yanbing, et al.
Published: (2025)
by: Zhang, Yanbing, et al.
Published: (2025)
Non-confusing Generation of Customized Concepts in Diffusion Models
by: Lin, Wang, et al.
Published: (2024)
by: Lin, Wang, et al.
Published: (2024)
Implicit Concept Removal of Diffusion Models
by: Liu, Zhili, et al.
Published: (2023)
by: Liu, Zhili, et al.
Published: (2023)
ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
by: Huang, Yuzhou, et al.
Published: (2025)
by: Huang, Yuzhou, et al.
Published: (2025)
Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations
by: Gong, Shizhan, et al.
Published: (2026)
by: Gong, Shizhan, et al.
Published: (2026)
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
by: Cendra, Fernando Julio, et al.
Published: (2025)
by: Cendra, Fernando Julio, et al.
Published: (2025)
FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition
by: Ding, Ganggui, et al.
Published: (2024)
by: Ding, Ganggui, et al.
Published: (2024)
CusEnhancer: A Zero-Shot Scene and Controllability Enhancement Method for Photo Customization via ResInversion
by: Ren, Maoye, et al.
Published: (2025)
by: Ren, Maoye, et al.
Published: (2025)
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
by: Tan, Andong, et al.
Published: (2024)
by: Tan, Andong, et al.
Published: (2024)
Pruning for Robust Concept Erasing in Diffusion Models
by: Yang, Tianyun, et al.
Published: (2024)
by: Yang, Tianyun, et al.
Published: (2024)
GrOCE:Graph-Guided Online Concept Erasure for Text-to-Image Diffusion Models
by: Han, Ning, et al.
Published: (2025)
by: Han, Ning, et al.
Published: (2025)
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models
by: Xie, Yan, et al.
Published: (2025)
by: Xie, Yan, et al.
Published: (2025)
Visual Concept-driven Image Generation with Text-to-Image Diffusion Model
by: Rahman, Tanzila, et al.
Published: (2024)
by: Rahman, Tanzila, et al.
Published: (2024)
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by: Wu, Yongliang, et al.
Published: (2024)
by: Wu, Yongliang, et al.
Published: (2024)
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery
by: Hao, Shaozhe, et al.
Published: (2023)
by: Hao, Shaozhe, et al.
Published: (2023)
Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models
by: Gong, Chao, et al.
Published: (2024)
by: Gong, Chao, et al.
Published: (2024)
CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization
by: Zhang, Lingyun, et al.
Published: (2024)
by: Zhang, Lingyun, et al.
Published: (2024)
Continuous Concepts Removal in Text-to-image Diffusion Models
by: Han, Tingxu, et al.
Published: (2024)
by: Han, Tingxu, et al.
Published: (2024)
LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models
by: Yang, Yang, et al.
Published: (2024)
by: Yang, Yang, et al.
Published: (2024)
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
by: Li, Ziqiang, et al.
Published: (2025)
by: Li, Ziqiang, et al.
Published: (2025)
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
by: Li, Ouxiang, et al.
Published: (2025)
by: Li, Ouxiang, et al.
Published: (2025)
Scaling Concept With Text-Guided Diffusion Models
by: Huang, Chao, et al.
Published: (2024)
by: Huang, Chao, et al.
Published: (2024)
ACE: Concept Editing in Diffusion Models without Performance Degradation
by: Wang, Ruipeng, et al.
Published: (2025)
by: Wang, Ruipeng, et al.
Published: (2025)
ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization
by: Kim, Minseo, et al.
Published: (2026)
by: Kim, Minseo, et al.
Published: (2026)
From Zero to Hero: Training-Free Custom Concept Spawning in World Models
by: Akdemir, Kiymet, et al.
Published: (2026)
by: Akdemir, Kiymet, et al.
Published: (2026)
Text Prompting for Multi-Concept Video Customization by Autoregressive Generation
by: Kothandaraman, Divya, et al.
Published: (2024)
by: Kothandaraman, Divya, et al.
Published: (2024)
MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance
by: Song, Wooseok, et al.
Published: (2025)
by: Song, Wooseok, et al.
Published: (2025)
ACE: Attentional Concept Erasure in Diffusion Models
by: Carter, Finn
Published: (2025)
by: Carter, Finn
Published: (2025)
Blending Concepts with Text-to-Image Diffusion Models
by: Olearo, Lorenzo, et al.
Published: (2025)
by: Olearo, Lorenzo, et al.
Published: (2025)
Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
by: Kowal, Matthew, et al.
Published: (2024)
by: Kowal, Matthew, et al.
Published: (2024)
Mass Concept Erasure in Diffusion Models with Concept Hierarchy
by: Tu, Jiahang, et al.
Published: (2026)
by: Tu, Jiahang, et al.
Published: (2026)
On the Concept Trustworthiness in Concept Bottleneck Models
by: Huang, Qihan, et al.
Published: (2024)
by: Huang, Qihan, et al.
Published: (2024)
Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
by: Xie, Yiwei, et al.
Published: (2026)
by: Xie, Yiwei, et al.
Published: (2026)
Adversarial Concept Distillation for One-Step Diffusion Personalization
by: Yang, Yixiong, et al.
Published: (2025)
by: Yang, Yixiong, et al.
Published: (2025)
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
by: Lin, Shen, et al.
Published: (2026)
by: Lin, Shen, et al.
Published: (2026)
DCBM: Data-Efficient Visual Concept Bottleneck Models
by: Prasse, Katharina, et al.
Published: (2024)
by: Prasse, Katharina, et al.
Published: (2024)
Human Preference-Aligned Concept Customization Benchmark via Decomposed Evaluation
by: Ishikawa, Reina, et al.
Published: (2025)
by: Ishikawa, Reina, et al.
Published: (2025)
Concept Unlearning by Modeling Key Steps of Diffusion Process
by: Zhang, Chaoshuo, et al.
Published: (2025)
by: Zhang, Chaoshuo, et al.
Published: (2025)
Similar Items
-
ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction
by: Hao, Shaozhe, et al.
Published: (2024) -
FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers
by: Zhang, Yanbing, et al.
Published: (2025) -
Non-confusing Generation of Customized Concepts in Diffusion Models
by: Lin, Wang, et al.
Published: (2024) -
Implicit Concept Removal of Diffusion Models
by: Liu, Zhili, et al.
Published: (2023) -
ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
by: Huang, Yuzhou, et al.
Published: (2025)