3 accepted papers
Instruction-based multimodal image manipulation has recently made rapid progress. However, existing evaluation methods lack a systematic and human-aligned framework for assessing model performance on complex and creative editing tasks. To address this gap, we propose CREval, a fully automated questi
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforce
Prompt learning for vision-language models, e.g. CLIP, is a rapidly advancing topic, focusing on leveraging pre-trained foundational models to generalize to downstream tasks with minimal training samples. Current prompt-tuning methods primarily optimize on general datasets, yet their application in…