activity
20222024
most citedTowards Efficient Visual Adaption via Structural Re-parameterization

28 citations · 41 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV20243 cited

Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

Gen Luo, Yiyi Zhou, Yuxin Zhang +3

Despite remarkable progress, existing multimodal large language models (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem…

cs.CV2024

Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks

Siyu Zou, Jiji Tang, Yiyi Zhou +5

Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most exis…

cs.CV20234 cited

Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models

Qiong Wu, Wei Yu, Yiyi Zhou +3

With ever increasing parameters and computation, vision-language pre-trained (VLP) models exhibit prohibitive expenditure in downstream task adaption. Recent endeavors mainly focus…

cs.CV202328 cited

Towards Efficient Visual Adaption via Structural Re-parameterization

Gen Luo, Minglang Huang, Yiyi Zhou +4

Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at inexpensively adapting large-scale pre-trained models to downstream tasks. Recent advances have a…

cs.CV2023

Active Teacher for Semi-Supervised Object Detection

Peng Mi, Jianghang Lin, Yiyi Zhou +7

In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{…

cs.CV20234 cited

Towards End-to-end Semi-supervised Learning for One-stage Object Detection

Gen Luo, Yiyi Zhou, Lei Jin +2

Semi-supervised object detection (SSOD) is a research hot spot in computer vision, which can greatly reduce the requirement for expensive bounding-box annotations. Despite great su…