activity
20202023
most citedDreamLLM: Synergistic Multimodal Comprehension and Creation

15 citations · 34 across the 8 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 15 cited

DreamLLM: Synergistic Multimodal Comprehension and Creation

Runpei Dong, Chunrui Han, Yuang Peng +11

This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multi…

cs.CL2023★ 1 cited

ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning

Liang Zhao, En Yu, Zheng Ge +8

Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact…

cs.CV2023

GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection

Weixin Mao, Jinrong Yang, Zheng Ge +5

Depth perception is a crucial component of monoc-ular 3D detection tasks that typically involve ill-posed problems. In light of the success of sample mining techniques in 2D object…

cs.CV2023★ 3 cited

Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception

Chunrui Han, Jinrong Yang, Jianjian Sun +6

Long-term temporal fusion is a crucial but often overlooked technique in camera-based Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner. While p…

cs.IR2023

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

Hongyu Zhou, Xin Zhou, Zhiwei Zeng +2

Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit in…

cs.IR2023★ 3 cited

Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation

Hongyu Zhou, Xin Zhou, Lingzi Zhang +1

User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets…