2 citations · 4 across the 10 of their papers we have counts for
13 papers
TraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding
Fan Yang, Shurong Zheng, Hongyin Zhao +5
Recent Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in image understanding and natural language generation. However, current approaches focus predominan…
GeM-VG: Towards Generalized Multi-image Visual Grounding with Multimodal Large Language Models
Shurong Zheng, Yousong Zhu, Hongyin Zhao +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive progress in single-image grounding and general multi-image understanding. Recently, some methods begin to addr…
Unleashing Perception-Time Scaling to Multimodal Reasoning Models
Yifan Li, Zhenghao Chen, Ziheng Wu +7
Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of L…
VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?
Jiachen Yu, Yufei Zhan, Ziheng Wu +3
Recent extensive works have demonstrated that by introducing long CoT, the capabilities of MLLMs to solve complex problems can be effectively enhanced. However, the reasons for the…
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
Zhentao He, Can Zhang, Ziheng Wu +6
Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompl…
FOCUS: Unified Vision-Language Modeling for Interactive Editing Driven by Referential Segmentation
Fan Yang, Yousong Zhu, Xin Li +6
Recent Large Vision Language Models (LVLMs) demonstrate promising capabilities in unifying visual understanding and generative modeling, enabling both accurate content understandin…
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Yufei Zhan, Ziheng Wu, Yousong Zhu +10
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general sc…
Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models
Yufei Zhan, Hongyin Zhao, Yousong Zhu +4
Large Multimodal Models (LMMs) have recently demonstrated remarkable visual understanding performance on both vision-language and vision-centric tasks. However, they often fall sho…
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
Yufei Zhan, Yousong Zhu, Shurong Zheng +4
Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the lang…
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
Yufei Zhan, Hongyin Zhao, Yousong Zhu +3
Large Multimodal Models (LMMs) have achieved significant breakthroughs in various vision-language and vision-centric tasks based on auto-regressive modeling. However, these models…
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Yufei Zhan, Shurong Zheng, Yousong Zhu +4
Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpassing the performance of ta…
Mitigating Hallucination in Visual Language Models with Visual Supervision
Zhiyang Chen, Yousong Zhu, Yufei Zhan +4
Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses that apparently contradict to the image content occasionally. The key problem lies in its…
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Yufei Zhan, Yousong Zhu, Zhiyang Chen +3
Replicating the innate human ability to detect all objects based on free-form texts at any granularity remains a formidable challenge for Large Vision Language Models (LVLMs). Curr…