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20232026
most citedMobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

12 citations · 23 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

Ziyu Ma, Hailang Huang, Shun Zou +5

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, exis…

cs.CV2025

UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement

Xiao Zhang, Fei Wei, Yong Wang +3

Zero-shot domain adaptation (ZSDA) presents substantial challenges due to the lack of images in the target domain. Previous approaches leverage Vision-Language Models (VLMs) to tac…

cs.CV2024

TIE: Revolutionizing Text-based Image Editing for Complex-Prompt Following and High-Fidelity Editing

Xinyu Zhang, Mengxue Kang, Fei Wei +3

As the field of image generation rapidly advances, traditional diffusion models and those integrated with multimodal large language models (LLMs) still encounter limitations in int…

cs.CV2024★ 10 cited

MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

Xiangxiang Chu, Limeng Qiao, Xinyu Zhang +8

We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an im…

cs.CV2023★ 12 cited

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Xiangxiang Chu, Limeng Qiao, Xinyang Lin +8

We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniq…

cs.CV2023★ 1 cited

Lenna: Language Enhanced Reasoning Detection Assistant

Fei Wei, Xinyu Zhang, Ailing Zhang +2

With the fast-paced development of multimodal large language models (MLLMs), we can now converse with AI systems in natural languages to understand images. However, the reasoning p…