2 citations · 3 across the 12 of their papers we have counts for
14 papers
LeWAM: Latent evolving World Action Model
Xueji Fang, Boqiang Duan, Hua Wu +2
World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). In VDM-based WAMs, observation…
FocusDiT: Masking Queries in Diffusion Transformers for Fine-grained Image Generation
Xueji Fang, Liyuan Ma, Jianhao Zeng +3
Diffusion transformer (DiT) has been widely adopted in the generative diffusion field, advancing the denoising of query tokens through attention and Feed-Forward (\text{FFN}) layer…
Equilibrated Diffusion: Frequency-aware Textual Embedding for Equilibrated Image Customization
Liyuan Ma, Xueji Fang, Guo-Jun Qi
Image customization learns target subjects from reference concept images and generates conditioned images per text prompts, mainly modifying styles or backgrounds. Prevailing metho…
When Images Speak Louder: Mitigating Language Bias-induced Hallucinations in VLMs through Cross-Modal Guidance
Jinjin Cao, Zhiyang Chen, Zijun Wang +3
Vision-Language Models (VLMs) have shown solid ability for multimodal understanding of both visual and language contexts. However, existing VLMs often face severe challenges of hal…
Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer
Han Zhou, Jinjin Cao, Liyuan Ma +2
Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated demonstrations. While human videos offe…
Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models
Zemin Huang, Yuhang Wang, Zhiyang Chen +1
Mask-based Diffusion Language Models (DLMs) struggle to revise incorrect tokens: once a token is generated, it typically remains fixed. The key challenge is to identify potential e…
C-Evolve: Consensus-based Evolution for Prompt Groups
Tiancheng Li, Yuhang Wang, Zhiyang Chen +3
Prompt evolution algorithms offer a powerful paradigm for enhancing AI systems based on closed-source models, while few work explores whether aggregating results from multiple prom…
SLOT: Sample-specific Language Model Optimization at Test-time
Yang Hu, Xingyu Zhang, Xueji Fang +4
We propose SLOT (Sample-specific Language Model Optimization at Test-time), a novel and parameter-efficient test-time inference approach that enhances a language model's ability to…
InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO
Xueji Fang, Liyuan Ma, Zhiyang Chen +2
Recent advances in text-to-video generation, particularly with autoregressive models, have enabled the synthesis of high-quality videos depicting individual scenes. However, extend…
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models
Zemin Huang, Zhiyang Chen, Zijun Wang +2
We introduce the Diffusion Chain of Lateral Thought (DCoLT), a reasoning framework for diffusion language models. DCoLT treats each intermediate step in the reverse diffusion proce…
Self-Guidance: Boosting Flow and Diffusion Generation on Their Own
Tiancheng Li, Weijian Luo, Zhiyang Chen +2
Proper guidance strategies are essential to achieve high-quality generation results without retraining diffusion and flow-based text-to-image models. Existing guidance either requi…
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation
Zilyu Ye, Zhiyang Chen, Tiancheng Li +3
Diffusion and flow matching models have achieved remarkable success in text-to-image generation. However, these models typically rely on the predetermined denoising schedules for a…
Flow Generator Matching
Zemin Huang, Zhengyang Geng, Weijian Luo +1
In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings…
One-Step Diffusion Distillation through Score Implicit Matching
Weijian Luo, Zemin Huang, Zhengyang Geng +2
Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples. This has motivated the…