6 papers
Representation Forcing for Bottleneck-Free Unified Multimodal Models
Yuqing Wang, Zhijie Lin, Ceyuan Yang +10
Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrained VAE for image generation…
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
Continuous Adversarial Flow Models
Shanchuan Lin, Ceyuan Yang, Zhijie Lin +2
We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-err…
Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning
Qinghao Ye, Xianhan Zeng, Fu Li +2
Image captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate det…
LLaVA-Critic: Learning to Evaluate Multimodal Models
Tianyi Xiong, Xiyao Wang, Dong Guo +5
We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks. LLaV…
Classification Done Right for Vision-Language Pre-Training
Zilong Huang, Qinghao Ye, Bingyi Kang +2
We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text…