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20242026
most citedParameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark

16 citations · 16 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

dMLLM-TTS: Self-Verified and Efficient Test-Time Scaling for Diffusion Multi-Modal Large Language Models

Yi Xin, Siqi Luo, Tianxiang Xu +13

Diffusion Multi-modal Large Language Models (dMLLMs) have recently emerged as a novel architecture unifying image generation and understanding. However, developing effective and ef…

cs.CV2025

LayerT2V: A Unified Multi-Layer Video Generation Framework

Guangzhao Li, Kangrui Cen, Baixuan Zhao +5

Text-to-video generation has advanced rapidly, but existing methods typically output only the final composited video and lack editable layered representations, limiting their use i…

cs.CV2025

TR-PTS: Task-Relevant Parameter and Token Selection for Efficient Tuning

Siqi Luo, Haoran Yang, Yi Xin +4

Large pre-trained models achieve remarkable performance in vision tasks but are impractical for fine-tuning due to high computational and storage costs. Parameter-Efficient Fine-Tu…

cs.CV2025

Resurrect Mask AutoRegressive Modeling for Efficient and Scalable Image Generation

Yi Xin, Le Zhuo, Qi Qin +8

AutoRegressive (AR) models have made notable progress in image generation, with Masked AutoRegressive (MAR) models gaining attention for their efficient parallel decoding. However,…

cs.CV2025

Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

Qi Qin, Le Zhuo, Yi Xin +20

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built u…

cs.CV2025

REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding

Yan Tai, Luhao Zhu, Yunan Ding +4

Multimodal Large Language Models (MLLMs) demonstrate robust zero-shot capabilities across diverse vision-language tasks after training on mega-scale datasets. However, dense predic…

cs.CV2025

Improving Video Generation with Human Feedback

Jie Liu, Gongye Liu, Jiajun Liang +14

Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this w…