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Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
Kai Zou, Ziqi Huang, Yuhao Dong +7
Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat…
RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark
Yang Shi, Yuhao Dong, Yue Ding +22
The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question rem…
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
Yuanhan Zhang, Yunice Chew, Yuhao Dong +3
Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation o…
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
Xinyu Huang, Yuhao Dong, Weiwei Tian +3
State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which ar…
ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
Hongbo Liu, Jingwen He, Yi Jin +11
Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrat…
Ola: Pushing the Frontiers of Omni-Modal Language Model
Zuyan Liu, Yuhao Dong, Jiahui Wang +4
Recent advances in large language models, particularly following GPT-4o, have sparked increasing interest in developing omni-modal models capable of understanding more modalities.…