2 citations · 5 across the 43 of their papers we have counts for
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MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems
Penghao Yin, Haomin Wang, Qihong Tang +3
Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning. Some recent methods introduce intermed…
Persistent Visual Memory: Sustaining Perception for Deep Generation in LVLMs
Siyuan Huang, Xiaoye Qu, Yafu Li +6
While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a "Visual Signal Dilution" phenomenon, where the accumul…
GEMS: Agent-Native Multimodal Generation with Memory and Skills
Zefeng He, Siyuan Huang, Xiaoye Qu +4
Recent multimodal generation models have achieved remarkable progress on general-purpose generation tasks, yet continue to struggle with complex instructions and specialized downst…
ExFusion: Efficient Transformer Training via Multi-Experts Fusion
Jiacheng Ruan, Daize Dong, Xiaoye Qu +5
Mixture-of-Experts (MoE) models substantially improve performance by increasing the capacity of dense architectures. However, directly training MoE models requires considerable com…
DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
Zefeng He, Xiaoye Qu, Yafu Li +3
While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leadin…
Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning
Guanjie Chen, Shirui Huang, Kai Liu +5
Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising…
VideoSSR: Video Self-Supervised Reinforcement Learning
Zefeng He, Xiaoye Qu, Yafu Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially advanced the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, the rapi…
Spotlight on Token Perception for Multimodal Reinforcement Learning
Siyuan Huang, Xiaoye Qu, Yafu Li +4
While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal rea…
FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting
Zefeng He, Xiaoye Qu, Yafu Li +3
While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame samplin…
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo +12
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
Zhaochen Su, Linjie Li, Mingyang Song +8
While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn similarly adaptive behaviors wit…
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
Mingyang Song, Xiaoye Qu, Jiawei Zhou +1
Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation. Despite this success, the training data of LVLMs…
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
Jihai Zhang, Xiaoye Qu, Tong Zhu +1
Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in multimodal intelligence. However, recent studies discovered that CLIP can only encode one aspect of the f…