2 citations · 2 across the 11 of their papers we have counts for
12 papers
Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning
Yu Xu, Yuxin Zhang, Lin Gao +3
A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric. Despite t…
Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
Runze He, Yiji Cheng, Tiankai Hang +11
In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful executi…
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
Yu Xu, Hongbin Yan, Juan Cao +11
Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise betwe…
JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization
Yunlong Lin, Linqing Wang, Kunjie Lin +11
Agent-based editing models have substantially advanced interactive experiences, processing quality, and creative flexibility. However, two critical challenges persist: (1) instruct…
UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation
Yibin Wang, Zhimin Li, Yuhang Zang +8
Recent progress in text-to-image (T2I) generation underscores the importance of reliable benchmarks in evaluating how accurately generated images reflect the semantics of their tex…
Arbitrary Generative Video Interpolation
Guozhen Zhang, Haiguang Wang, Chunyu Wang +3
Video frame interpolation (VFI), which generates intermediate frames from given start and end frames, has become a fundamental function in video generation applications. However, e…
Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference
Xiangwei Shen, Zhimin Li, Zhantao Yang +6
Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward. However, they exhibit two primary chall…
Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning
Yibin Wang, Zhimin Li, Yuhang Zang +6
Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods usi…
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation
Xinran Wang, Muxi Diao, Yuanzhi Liu +4
Training text-to-image (T2I) models with detailed captions can significantly improve their generation quality. Existing methods often rely on simplistic metrics like caption length…
Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning
Yibin Wang, Zhimin Li, Yuhang Zang +4
Recent advances in multimodal Reward Models (RMs) have shown significant promise in delivering reward signals to align vision models with human preferences. However, current RMs ar…
Enhancing Memory Efficiency in Large Language Model Training Through Chronos-aware Pipeline Parallelism
Xinyuan Lin, Chenlu Li, Zongle Huang +5
Larger model sizes and longer sequence lengths have empowered the Large Language Model (LLM) to achieve outstanding performance across various domains. However, this progress bring…
LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation
Weiquan Huang, Aoqi Wu, Yifan Yang +10
CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption pairs. Inspired by the rap…