12 papers
Vision as Unified Multimodal Generation
Xiaoyang Han, Jianhua Li, Kewang Deng +14
We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal…
Scaling Spatial Intelligence with Multimodal Foundation Models
Zhongang Cai, Ruisi Wang, Chenyang Gu +26
Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation m…
UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?
Zimo Wen, Boxiu Li, Wanbo Zhang +11
Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lac…
OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
Feilong Tang, Xiang An, Yunyao Yan +16
Hypothesis. Artificial general intelligence is, at its core, a compression problem. Effective compression demands resonance: deep learning scales best when its architecture aligns…
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
Xiang An, Yin Xie, Kaicheng Yang +20
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial co…
Visual Jigsaw Post-Training Improves MLLMs
Penghao Wu, Yushan Zhang, Haiwen Diao +3
Reinforcement learning based post-training has recently emerged as a powerful paradigm for enhancing the alignment and reasoning capabilities of multimodal large language models (M…