33 citations · 44 across the 11 of their papers we have counts for
13 papers
Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System
Penghao Wu, Haiwen Diao, Weichen Fan +3
While unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model, functional unification does not guarantee learning synergy: the tw…
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…
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…
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…
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…
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…
MMSearch-R1: Incentivizing LMMs to Search
Jinming Wu, Zihao Deng, Wei Li +5
Robust deployment of large multimodal models (LMMs) in real-world scenarios requires access to external knowledge sources, given the complexity and dynamic nature of real-world inf…
Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos
Kairui Hu, Penghao Wu, Fanyi Pu +5
Humans acquire knowledge through three cognitive stages: perceiving information, comprehending knowledge, and adapting knowledge to solve novel problems. Videos serve as an effecti…
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin +9
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Build…
Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Kaichen Zhang, Yifei Shen, Bo Li +1
Recent advances in Large Multimodal Models (LMMs) lead to significant breakthroughs in both academia and industry. One question that arises is how we, as humans, can understand the…
LLaVA-Video: Video Instruction Tuning With Synthetic Data
Yuanhan Zhang, Jinming Wu, Wei Li +4
The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we pro…
LLaVA-OneVision: Easy Visual Task Transfer
Bo Li, Yuanhan Zhang, Dong Guo +8
We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT…