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20232026
most citedMMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

18 citations · 24 across the 18 of their papers we have counts for

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16 papers · 1 filter

cs.CV2025

MILR: Improving Multimodal Image Generation via Test-Time Latent Reasoning

Yapeng Mi, Yanpeng Zhao, Hengli Li +6

Reasoning-augmented machine learning systems have shown improved performance in various domains, including image generation. However, existing reasoning-based methods for image gen…

cs.CV2025

NEP: Autoregressive Image Editing via Next Editing Token Prediction

Huimin Wu, Xiaojian Ma, Haozhe Zhao +2

Text-guided image editing involves modifying a source image based on a language instruction and, typically, requires changes to only small local regions. However, existing approach…

cs.CV2025

Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation

Ziyu Zhu, Xilin Wang, Yixuan Li +9

Embodied scene understanding requires not only comprehending visual-spatial information that has been observed but also determining where to explore next in the 3D physical world.…

cs.CV2025

LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning

Jiangyong Huang, Xiaojian Ma, Xiongkun Linghu +6

Developing vision-language models (VLMs) capable of understanding 3D scenes has been a longstanding research goal. Despite recent progress, 3D VLMs still struggle with spatial reas…

cs.CV2025

From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes

Tianxu Wang, Zhuofan Zhang, Ziyu Zhu +5

3D visual grounding has made notable progress in localizing objects within complex 3D scenes. However, grounding referring expressions beyond objects in 3D scenes remains unexplore…

cs.CV2025

Building LLM Agents by Incorporating Insights from Computer Systems

Yapeng Mi, Zhi Gao, Xiaojian Ma +1

LLM-driven autonomous agents have emerged as a promising direction in recent years. However, many of these LLM agents are designed empirically or based on intuition, often lacking…

cs.CV2025

Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning

Pengxiang Li, Zhi Gao, Bofei Zhang +8

Multimodal agents, which integrate a controller e.g., a vision language model) with external tools, have demonstrated remarkable capabilities in tackling complex multimodal tasks.…

cs.CV2025

TongUI: Internet-Scale Trajectories from Multimodal Web Tutorials for Generalized GUI Agents

Bofei Zhang, Zirui Shang, Zhi Gao +7

Building Graphical User Interface (GUI) agents is a promising research direction, which simulates human interaction with computers or mobile phones to perform diverse GUI tasks. Ho…

cs.CV2025

Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding

Yue Fan, Xiaojian Ma, Rongpeng Su +4

This paper investigates the problem of understanding dynamic 3D scenes from egocentric observations, a key challenge in robotics and embodied AI. Unlike prior studies that explored…

cs.CV2025

LongViTU: Instruction Tuning for Long-Form Video Understanding

Rujie Wu, Xiaojian Ma, Hai Ci +5

This paper introduces LongViTU, a large-scale (~121k QA pairs, ~900h videos), automatically generated dataset for long-form video understanding. We propose a systematic approach th…

cs.CV2024★ 1 cited

Task-oriented Sequential Grounding and Navigation in 3D Scenes

Zhuofan Zhang, Ziyu Zhu, Junhao Li +8

Grounding natural language in 3D environments is a critical step toward achieving robust 3D vision-language alignment. Current datasets and models for 3D visual grounding predomina…

cs.CV2024★ 1 cited

UltraEdit: Instruction-based Fine-Grained Image Editing at Scale

Haozhe Zhao, Xiaojian Ma, Liang Chen +7

This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to addr…

cs.CV2024

Unifying 3D Vision-Language Understanding via Promptable Queries

Ziyu Zhu, Zhuofan Zhang, Xiaojian Ma +6

A unified model for 3D vision-language (3D-VL) understanding is expected to take various scene representations and perform a wide range of tasks in a 3D scene. However, a considera…

cs.CV2024★ 2 cited

Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting

Jun Guo, Xiaojian Ma, Yue Fan +2

Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, with wide-ranging applications in embodied agents and augmented reality systems. Existin…

cs.CV2024★ 2 cited

VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding

Yue Fan, Xiaojian Ma, Rujie Wu +4

We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video und…

cs.CV2023

CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update

Zhi Gao, Yuntao Du, Xintong Zhang +4

Utilizing large language models (LLMs) to compose off-the-shelf visual tools represents a promising avenue of research for developing robust visual assistants capable of addressing…