29 citations · 37 across the 12 of their papers we have counts for
14 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…
From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Fan Yang, Zhiyang Chen, Yousong Zhu +2
Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-v…
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…
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…
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…
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
Yu Qi, Fan Yang, Yousong Zhu +4
Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved com…
MST: Masked Self-Supervised Transformer for Visual Representation
Zhaowen Li, Zhiyang Chen, Fan Yang +8
Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual…
Adaptive Class Suppression Loss for Long-Tail Object Detection
Tong Wang, Yousong Zhu, Chaoyang Zhao +3
To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat…