16 citations · 96 across the 68 of their papers we have counts for
8 papers · 1 filter
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
Zhangyang Qi, Ye Fang, Mengchen Zhang +6
The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper…
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Zeyi Sun, Ye Fang, Tong Wu +6
Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual mod…
HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image
Tong Wu, Zhibing Li, Shuai Yang +5
3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing…
OneLLM: One Framework to Align All Modalities with Language
Jiaming Han, Kaixiong Gong, Yiyuan Zhang +6
Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However, existing works rely heavily on modality…
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Zhangyang Qi, Ye Fang, Zeyi Sun +5
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting p…
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
Lin Chen, Jinsong Li, Xiaoyi Dong +5
In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data. To address this bott…
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation
Qidong Huang, Xiaoyi Dong, Pan Zhang +6
Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing…
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
Shuangrui Ding, Rui Qian, Haohang Xu +2
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS). Our key insight is that the inherent structural dependencies present…