2 citations · 2 across the 6 of their papers we have counts for
9 papers
FIRM-WM: State-factorized factual-interventional recurrent modeling for reward-free visual planning
Yilun Wu, Yunjian Zhang, Aobo Li +3
Reward-free latent world models can learn from offline videos and solve new image--goal tasks by optimizing actions through predicted latent futures. This setting places two demand…
HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models
Yun-Jian Zhang, Chen-Wei Liang, Tian-Yi Zhang +7
World models enable language-model agents to predict environment dynamics and plan before acting. In text environments, the model must learn symbolic action effects from serialized…
HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
Hong-Yu An, Yun-Jian Zhang, Chen-Wei Liang +7
Multi-hop question answering requires systems to retrieve evidence from multiple documents and connect scattered facts into a coherent reasoning process. Standard retrieval-augment…
Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling
Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo +2
Modern text-to-image models produce impressive visual results from richly specified prompts, yet their behavior under long prompts remains insufficiently understood. In this paper,…
Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
Yi-Lun Wu, Bo-Kai Ruan, Chiang Tseng +1
Direct preference optimization (DPO) methods have shown strong potential in aligning text-to-image diffusion models with human preferences by training on paired comparisons. These…
TF-TI2I: Training-Free Text-and-Image-to-Image Generation via Multi-Modal Implicit-Context Learning in Text-to-Image Models
Teng-Fang Hsiao, Bo-Kai Ruan, Yi-Lun Wu +2
Text-and-Image-To-Image (TI2I), an extension of Text-To-Image (T2I), integrates image inputs with textual instructions to enhance image generation. Existing methods often partially…
FreeCond: Free Lunch in the Input Conditions of Text-Guided Inpainting
Teng-Fang Hsiao, Bo-Kai Ruan, Sung-Lin Tsai +2
In this study, we aim to determine and solve the deficiency of Stable Diffusion Inpainting (SDI) in following the instruction of both prompt and mask. Due to the training bias from…
Gradient Normalization for Generative Adversarial Networks
Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam +1
In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the…
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
Yun-Zhu Song, Hong-Han Shuai, Sung-Lin Yeh +3
With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be ov…