69 citations · 127 across the 30 of their papers we have counts for
16 papers · 1 filter
Semantic Steering for Controllable Generation: Tuning-Free Concept Erasure in Multimodal Diffusion Transformers
Qiao Li, Xiaomeng Fu, Yuanshu Zhao +3
Multimodal Diffusion Transformers (MM-DiTs) have demonstrated remarkable text-to-image generation performance, surpassing traditional U-Net-based diffusion models. Nevertheless, th…
Erase but Preserve: Controllable Removal of Copyrighted Animation Characters via Optimized Semantic Anchors
Qiao Li, Xiaomeng Fu, Wangjia Yu +4
The exceptional generation capabilities of text-to-image diffusion models have raised copyright concerns, particularly the unauthorized reproduction of animation characters. Existi…
Towards Memory-Efficient Autoregressive Video Generation via Instance-Specific Parametric Absorption
Xiaomeng Fu, Jia Li, Yiming Hu +5
Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottlenec…
Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
Runze He, Yiji Cheng, Tiankai Hang +11
In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful executi…
Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation
Wenchao Zhang, Jiahe Tian, Runze He +5
Recent text-to-image (T2I) generation models have advanced significantly, enabling the creation of high-fidelity images from textual prompts. However, existing evaluation benchmark…
Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression
Shaofei Huang, Zhenwei Shen, Zehao Huang +4
In this paper, we focus on the challenging task of monocular 3D lane detection. Previous methods typically adopt inverse perspective mapping (IPM) to transform the Front-Viewed (FV…
Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation
Baisen Wang, Le Zhuo, Zhaokai Wang +7
Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fu…
FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction
Runze He, Kai Ma, Linjiang Huang +6
Introducing user-specified visual concepts in image editing is highly practical as these concepts convey the user's intent more precisely than text-based descriptions. We propose F…
Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding
Hongyu Li, Tianrui Hui, Zihan Ding +5
Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous…
Learning to Discover Forgery Cues for Face Forgery Detection
Jiahe Tian, Peng Chen, Cai Yu +4
Locating manipulation maps, i.e., pixel-level annotation of forgery cues, is crucial for providing interpretable detection results in face forgery detection. Related learning objec…
Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Audio and Language Referenced Video Object Segmentation
Shaofei Huang, Rui Ling, Hongyu Li +5
In this paper, we propose an Audio-Language-Referenced SAM 2 (AL-Ref-SAM 2) pipeline to explore the training-free paradigm for audio and language-referenced video object segmentati…
Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models
Qiao Li, Xiaomeng Fu, Xi Wang +4
With the rapid advancements of large-scale text-to-image diffusion models, various practical applications have emerged, bringing significant convenience to society. However, model…
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
Cai Yu, Shan Jia, Xiaomeng Fu +6
With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes. While effective in their specific…
Model Will Tell: Training Membership Inference for Diffusion Models
Xiaomeng Fu, Xi Wang, Qiao Li +3
Diffusion models pose risks of privacy breaches and copyright disputes, primarily stemming from the potential utilization of unauthorized data during the training phase. The Traini…
Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
Runze He, Shaofei Huang, Xuecheng Nie +6
In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt…
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval
Xing Wu, Chaochen Gao, Zijia Lin +3
Video language pre-training methods have mainly adopted sparse sampling techniques to alleviate the temporal redundancy of videos. Though effective, sparse sampling still suffers i…