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20202026
most citedExploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

20 citations · 49 across the 22 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CV2024

Ponder & Press: Advancing Visual GUI Agent towards General Computer Control

Yiqin Wang, Haoji Zhang, Jingqi Tian +1

Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting their flexibility across diverse software environments and pla…

cs.CV2024★ 1 cited

InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning

Xiaotian Han, Yiren Jian, Xuefeng Hu +8

Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as ma…

cs.CV2024★ 1 cited

Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model

Benlin Liu, Yuhao Dong, Yiqin Wang +6

Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. C…

cs.CV2024

Hierarchical Memory for Long Video QA

Yiqin Wang, Haoji Zhang, Yansong Tang +4

This paper describes our champion solution to the LOVEU Challenge @ CVPR'24, Track 1 (Long Video VQA). Processing long sequences of visual tokens is computationally expensive and m…

cs.CV2024

Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams

Haoji Zhang, Yiqin Wang, Yansong Tang +4

Benefiting from the advancements in large language models and cross-modal alignment, existing multi-modal video understanding methods have achieved prominent performance in offline…

cs.CV2024

InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding

Haogeng Liu, Quanzeng You, Xiaotian Han +7

Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intri…

cs.CL2024★ 20 cited

Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

Yiqi Wang, Wentao Chen, Xiaotian Han +7

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large…

cs.CV2024

COCO is "ALL'' You Need for Visual Instruction Fine-tuning

Xiaotian Han, Yiqi Wang, Bohan Zhai +2

Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. Visual instruction fine-tuning (IFT) is a vital process for aligning M…