7 papers
VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?
Qing'an Liu, Juntong Feng, Yuhao Wang +6
Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pur…
TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models
Harold Haodong Chen, Disen Lan, Wen-Jie Shu +10
The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consi…
Interleaving Reasoning for Better Text-to-Image Generation
Wenxuan Huang, Shuang Chen, Zheyong Xie +15
Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction followin…
Are Unified Vision-Language Models Necessary: Generalization Across Understanding and Generation
Jihai Zhang, Tianle Li, Linjie Li +2
Recent advancements in unified vision-language models (VLMs), which integrate both visual understanding and generation capabilities, have attracted significant attention. The under…
Unveiling the Compositional Ability Gap in Vision-Language Reasoning Model
Tianle Li, Jihai Zhang, Yongming Rao +1
While large language models (LLMs) demonstrate strong reasoning capabilities utilizing reinforcement learning (RL) with verifiable reward, whether large vision-language models (VLM…
Visually Interpretable Subtask Reasoning for Visual Question Answering
Yu Cheng, Arushi Goel, Hakan Bilen
Answering complex visual questions like `Which red furniture can be used for sitting?' requires multi-step reasoning, including object recognition, attribute filtering, and relatio…