Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Mengting Chen, Zhengrui Chen, Yongchao Du +16 authors
A commercial-scale virtual try-on system achieves high success rates, photorealistic results, and real-time performance through integrated system design and multi-stage training.
NextStep Team, Chunrui Han, Guopeng Li +47 authors
NextStep-1, a 14B autoregressive model with a 157M flow matching head, achieves state-of-the-art performance in text-to-image generation and image editing by processing discrete text tokens and continuous image tokens.
Xiangyan Qu, Zhenlong Yuan, Jing Tang +9 authors
Image-CoT methods are extended to image editing with ADE-CoT, which improves efficiency and performance through adaptive resource allocation, edit-specific verification, and opportunistic stopping mechanisms.
Bing Zhao, Chenfei Wu, Deqing Li +72 authors
Qwen-Image-2.0 is an advanced image generation model that combines high-fidelity synthesis with precise editing capabilities through a unified framework using Qwen3-VL as condition encoder and Multimodal Diffusion Transformer for joint modeling.
Shitao Xiao, Yueze Wang, Junjie Zhou +6 authors
OmniGen is a unified diffusion model for image generation that supports diverse tasks without additional modules, emphasizing simplicity, knowledge transfer, and reasoning capabilities.
Thaddäus Wiedemer, Yuxuan Li, Paul Vicol +6 authors
Veo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.
Team Seedream, Yunpeng Chen, Yu Gao +47 authors
Seedream 4.0 is a high-performance multimodal image generation system that integrates text-to-image synthesis, image editing, and multi-image composition using a diffusion transformer and VAE, achieving state-of-the-art results with efficient training and inference.
Dianyi Wang, Ruihang Li, Feng Han +17 authors
DeepGen 1.0 is a lightweight 5B unified multimodal model that achieves superior performance in image generation and editing through advanced alignment techniques and progressive training strategies.
Dianyi Wang, Chaofan Ma, Feng Han +8 authors
UniReason integrates text-to-image generation and image editing through a dual reasoning paradigm that enhances planning with world knowledge and uses editing for visual refinement, achieving superior performance on reasoning-intensive benchmarks.
Zhiheng Liu, Weiming Ren, Haozhe Liu +22 authors
TUNA, a unified multimodal model, uses a cascaded VAE and representation encoder for end-to-end multimodal understanding and generation, outperforming decoupled models and achieving state-of-the-art results across various benchmarks.
Keyu Tian, Yi Jiang, Zehuan Yuan +2 authors
Visual AutoRegressive (VAR) modeling improves image generation performance and speed over autoregressive and diffusion transformer models, achieving superior results in image quality, data efficiency, and scalability.
Axel Sauer, Frederic Boesel, Tim Dockhorn +3 authors
Latent Adversarial Diffusion Distillation (LADD) simplifies the training and improves the performance of diffusion models by using generative features from latent diffusion models, enabling fast and high-resolution image synthesis.
Denis Bobkov, Vadim Titov, Aibek Alanov +1 authors
StyleFeatureEditor combines W-space and F-space for effective image attribute manipulation in StyleGAN, balancing reconstruction quality and detailed editing.
Peiyu Wang, Yi Peng, Yimeng Gan +12 authors
Skywork UniPic, a 1.5 billion-parameter autoregressive model, unifies image understanding, text-to-image generation, and image editing with state-of-the-art performance on commodity hardware.
Guo-Hua Wang, Shanshan Zhao, Xinjie Zhang +9 authors
Ovis-U1, a 3-billion-parameter unified model, integrates multimodal understanding, text-to-image generation, and image editing using a diffusion-based visual decoder and bidirectional token refiner, achieving state-of-the-art performance across various benchmarks.
Wei-Ge Chen, Irina Spiridonova, Jianwei Yang +2 authors
LLaVA-Interactive is a cost-efficient multimodal dialogue system that integrates visual chat, image segmentation, and generation capabilities to enhance human-AI interaction.
Chong Mou, Xintao Wang, Jiechong Song +2 authors
DragonDiffusion, a novel image editing method, enables precise manipulation of generated or real images using classifier guidance and multi-scale attention mechanisms without requiring fine-tuning.
Kangfu Mei, Mauricio Delbracio, Hossein Talebi +3 authors
A novel single-stage distillation method for generative diffusion models reduces sampling time while maintaining performance across tasks like super-resolution and image editing.
Chaoning Zhang, Dongshen Han, Yu Qiao +4 authors
MobileSAM enhances the Segment Anything Model (SAM) for mobile devices by decoupling the image encoder and mask decoder to create a lightweight version that maintains performance while significantly reducing size and improving speed.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号