2 citations · 2 across the 11 of their papers we have counts for
13 papers
When World Models Dream Wrong: Physical-Conditioned Adversarial Attacks against World Models
Zhixiang Guo, Siyuan Liang, Andras Balogh +4
Generative world models (WMs) are increasingly used to synthesize controllable, sensor-conditioned driving videos, yet their reliance on physical priors exposes novel attack surfac…
SPA-Cache: Singular Proxies for Adaptive Caching in Diffusion Language Models
Wenhao Sun, Rong-Cheng Tu, Yifu Ding +4
While Diffusion Language Models (DLMs) offer a flexible, arbitrary-order alternative to the autoregressive paradigm, their non-causal nature precludes standard KV caching, forcing…
VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning
Yibo Wang, Yongcheng Jing, Shunyu Liu +5
Long-context reasoning has significantly empowered large language models (LLMs) to tackle complex tasks, yet it introduces severe efficiency bottlenecks due to the computational co…
Lightning Fast Caching-based Parallel Denoising Prediction for Accelerating Talking Head Generation
Jianzhi Long, Wenhao Sun, Rongcheng Tu +1
Diffusion-based talking head models generate high-quality, photorealistic videos but suffer from slow inference, limiting practical applications. Existing acceleration methods for…
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrain…
SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding
Zhao Jin, Rong-Cheng Tu, Jingyi Liao +4
3D Visual Grounding (3DVG) aims to localize target objects within a 3D scene based on natural language queries. To alleviate the reliance on costly 3D training data, recent studies…
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval
Rong-Cheng Tu, Zhao Jin, Jingyi Liao +4
Existing Zero-Shot Composed Image Retrieval (ZS-CIR) methods typically train adapters that convert reference images into pseudo-text tokens, which are concatenated with the modifyi…
VORTA: Efficient Video Diffusion via Routing Sparse Attention
Wenhao Sun, Rong-Cheng Tu, Yifu Ding +4
Video diffusion transformers have achieved remarkable progress in high-quality video generation, but remain computationally expensive due to the quadratic complexity of attention o…
Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
Jingyi Liao, Xun Xu, Yongyi Su +4
Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor…
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Wenhao Sun, Rong-Cheng Tu, Jingyi Liao +2
Diffusion Transformers (DiTs) have proven effective in generating high-quality videos but are hindered by high computational costs. Existing video DiT sampling acceleration methods…
SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing
Rong-Cheng Tu, Wenhao Sun, Zhao Jin +3
While open-source video generation and editing models have made significant progress, individual models are typically limited to specific tasks, failing to meet the diverse needs o…
Diffusion Model-Based Video Editing: A Survey
Wenhao Sun, Rong-Cheng Tu, Jingyi Liao +1
The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making "what you want is what you see" a reality. Among these, video editin…
A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
Xincheng Shuai, Henghui Ding, Xingjun Ma +3
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field…