2 citations · 3 across the 5 of their papers we have counts for
5 papers
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks
Yan Yang, Dongxu Li, Haoning Wu +4
Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluati…
Generative Frame Sampler for Long Video Understanding
Linli Yao, Haoning Wu, Kun Ouyang +5
Despite recent advances in Video Large Language Models (VideoLLMs), effectively understanding long-form videos remains a significant challenge. Perceiving lengthy videos containing…
Aria-UI: Visual Grounding for GUI Instructions
Yuhao Yang, Yue Wang, Dongxu Li +4
Digital agents for automating tasks across different platforms by directly manipulating the GUIs are increasingly important. For these agents, grounding from language instructions…
Aria: An Open Multimodal Native Mixture-of-Experts Model
Dongxu Li, Yudong Liu, Haoning Wu +17
Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary mu…
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
Haoning Wu, Dongxu Li, Bei Chen +1
Large multimodal models (LMMs) are processing increasingly longer and richer inputs. Albeit the progress, few public benchmark is available to measure such development. To mitigate…