18 papers · 1 filter
Evidence-Aligned Multimodal On-Policy Self-Distillation for Fine-Grained Visual Understanding
Nanxing Hu, Qiwei Yan, Jinchao Zhang +1
Fine-grained visual understanding requires models to recognize small details within complex images. Multimodal on-policy self-distillation (OPSD) addresses this challenge by using…
Summarize Before Grounding: Query-Guided Chunk Condensation for Long-Video Temporal Grounding
Nanxing Hu, Xiaoyue Duan, Qiwei Yan +3
Video temporal grounding (VTG) aims to localize the video interval corresponding to a language query. Recent large vision-language models (LVLMs) show great potential in solving su…
Trustworthy synthetic visual media: Evidence across the media lifecycle
Zexi Jia, Zhiqiang Yuan, Jie Zhou +1
Images and videos have long helped people understand what happened and how a work came into being. Generative systems complicate that role. Realistic media can now be produced and…
RAVA: Retrieval-Augmented Viewpoint Alignment for Subject-Driven Image Generation
Qiwei Yan, Zhiqiang Yuan, Chongyang Li +4
Reference-driven image generation has made rapid progress on identity preservation, but reliable viewpoint control across different subjects remains poorly understood. The difficul…
CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection
Zexi Jia, Zhiqiang Yuan, Xiaoyue Duan +3
AI-generated image detection faces a persistent trade-off between generalization and efficiency: lightweight artifact-based methods often degrade on unseen generators or domains, w…
Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
Zexi Jia, Pengcheng Luo, Zhengyao Fang +2
Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, an…
Evaluating Generative Models via One-Dimensional Code Distributions
Zexi Jia, Pengcheng Luo, Yijia Zhong +2
Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant…
Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity
Zhengyao Fang, Zexi Jia, Yijia Zhong +5
Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challe…
Exploring Specular Reflection Inconsistency for Generalizable Face Forgery Detection
Hongyan Fei, Zexi Jia, Chuanwei Huang +2
Detecting deepfakes has become increasingly challenging as forgery faces synthesized by AI-generated methods, particularly diffusion models, achieve unprecedented quality and resol…
StyleDecoupler: Generalizable Artistic Style Disentanglement
Zexi Jia, Jinchao Zhang, Jie Zhou
Representing artistic style is challenging due to its deep entanglement with semantic content. We propose StyleDecoupler, an information-theoretic framework that leverages a key in…
F2RVLM: Boosting Fine-grained Fragment Retrieval for Multi-Modal Long-form Dialogue with Vision Language Model
Hanbo Bi, Zhiqiang Yuan, Zexi Jia +6
Traditional dialogue retrieval aims to select the most appropriate utterance or image from recent dialogue history. However, they often fail to meet users' actual needs for revisit…
From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
Zexi Jia, Chuanwei Huang, Yeshuang Zhu +6
Current legal frameworks consider AI-generated works eligible for copyright protection when they meet originality requirements and involve substantial human intellectual input. How…
A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets
Zexi Jia, Chuanwei Huang, Hongyan Fei +6
Vision-language models (VLMs) often struggle with compositional reasoning due to insufficient high-quality image-text data. To tackle this challenge, we propose a novel block-based…
Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models
Nanxing Hu, Xiaoyue Duan, Jinchao Zhang +1
Large Vision-Language Models (LVLMs) usually generate texts which satisfy context coherence but don't match the visual input. Such a hallucination issue hinders LVLMs' applicabilit…
Control-CLIP: Decoupling Category and Style Guidance in CLIP for Specific-Domain Generation
Zexi Jia, Chuanwei Huang, Hongyan Fei +4
Text-to-image diffusion models have shown remarkable capabilities of generating high-quality images closely aligned with textual inputs. However, the effectiveness of text guidance…
Semantic to Structure: Learning Structural Representations for Infringement Detection
Chuanwei Huang, Zexi Jia, Hongyan Fei +4
Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly in…
ILDiff: Generate Transparent Animated Stickers by Implicit Layout Distillation
Ting Zhang, Zhiqiang Yuan, Yeshuang Zhu +1
High-quality animated stickers usually contain transparent channels, which are often ignored by current video generation models. To generate fine-grained animated transparency chan…
WalkVLM:Aid Visually Impaired People Walking by Vision Language Model
Zhiqiang Yuan, Ting Zhang, Ying Deng +5
Approximately 200 million individuals around the world suffer from varying degrees of visual impairment, making it crucial to leverage AI technology to offer walking assistance for…