1 citations · 4 across the 16 of their papers we have counts for
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AnchorEdit: Maintaining Temporal Consistency in Multi-turn Image Editing via Causal Memory
Hang Xu, Xiaoxiao Ma, Guohui Zhang +7
Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps. While existing resea…
SCOPE: Structured Decomposition and Conditional Skill Orchestration for Complex Image Generation
Tianfei Ren, Zhipeng Yan, Yiming Zhao +13
While text-to-image models have made strong progress in visual fidelity, faithfully realizing complex visual intents remains challenging because many requirements must be tracked a…
MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation
Xiaoxiao Ma, Jiachen Lei, Tianfei Ren +6
Reinforcement learning (RL) has been successfully applied to autoregressive (AR) and diffusion models. However, extending RL to hybrid AR-diffusion frameworks remains challenging d…
Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
Zhen Zou, Xiaoxiao Ma, Mingde Yao +3
Autoregressive (AR)-Diffusion hybrid paradigms combine AR's structured semantic modeling with diffusion's high-fidelity synthesis, yet suffer from a dual speed bottleneck: the sequ…
VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs
Naishan Zheng, Jie Huang, Qingpei Guo +1
Understanding long videos with multimodal large language models (MLLMs) remains challenging due to the heavy redundancy across frames and the need for temporally coherent represent…
Fast-ARDiff: An Entropy-informed Acceleration Framework for Continuous Space Autoregressive Generation
Zhen Zou, Xiaoxiao Ma, Jie Huang +2
Autoregressive(AR)-diffusion hybrid paradigms combine AR's structured modeling with diffusion's photorealistic synthesis, yet suffer from high latency due to sequential AR generati…
Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy
Xiaoxiao Ma, Feng Zhao, Pengyang Ling +6
In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower informat…
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Zizheng Yang, Hu Yu, Bing Li +3
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…
InfoScale: Unleashing Training-free Variable-scaled Image Generation via Effective Utilization of Information
Guohui Zhang, Jiangtong Tan, Linjiang Huang +4
Diffusion models (DMs) have become dominant in visual generation but suffer performance drop when tested on resolutions that differ from the training scale, whether lower or higher…
SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models
Hu Yu, Hao Luo, Xueyang Fu +3
Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation. However, their iterative sampling mechanism results in slow inference speeds. While…
FreePCA: Integrating Consistency Information across Long-short Frames in Training-free Long Video Generation via Principal Component Analysis
Jiangtong Tan, Hu Yu, Jie Huang +2
Long video generation involves generating extended videos using models trained on short videos, suffering from distribution shifts due to varying frame counts. It necessitates the…
DiffLoss: unleashing diffusion model as constraint for training image restoration network
Jiangtong Tan, Feng Zhao
Image restoration aims to enhance low quality images, producing high quality images that exhibit natural visual characteristics and fine semantic attributes. Recently, the diffusio…
FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining
Zou Zhen, Yu Hu, Zhao Feng
Images corrupted by rain streaks often lose vital frequency information for perception, and image deraining aims to solve this issue which relies on global and local degradation mo…
Unmasking Bias in Diffusion Model Training
Hu Yu, Li Shen, Jie Huang +2
Denoising diffusion models have emerged as a dominant approach for image generation, however they still suffer from slow convergence in training and color shift issues in sampling.…