5 papers
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…
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…
BREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries
Tianle Li, Yongming Rao, Winston Hu +1
Encoder-free multimodal large language models(MLLMs) eliminate the need for a well-trained vision encoder by directly processing image tokens before the language model. While this…