29 citations · 119 across the 11 of their papers we have counts for
11 papers · 1 filter
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
Taolin Zhang, Jiawang Bai, Zhihe Lu +4
Recent works on parameter-efficient transfer learning (PETL) show the potential to adapt a pre-trained Vision Transformer to downstream recognition tasks with only a few learnable…
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
Zhihe Lu, Jiawang Bai, Xin Li +2
Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performan…
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
Xin Li, Dongze Lian, Zhihe Lu +3
Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additiona…
Evolving Knowledge Mining for Class Incremental Segmentation
Zhihe Lu, Shuicheng Yan, Xinchao Wang
Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great significance in real-world applications. Although the existing CISS methods demonstrate re…
A Dive into SAM Prior in Image Restoration
Zeyu Xiao, Jiawang Bai, Zhihe Lu +1
The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solu…
Can SAM Boost Video Super-Resolution?
Zhihe Lu, Zeyu Xiao, Jiawang Bai +2
The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple fra…
Task Residual for Tuning Vision-Language Models
Tao Yu, Zhihe Lu, Xin Jin +2
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned kn…
Prediction Calibration for Generalized Few-shot Semantic Segmentation
Zhihe Lu, Sen He, Da Li +2
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of…
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
Zhihe Lu, Sen He, Xiatian Zhu +3
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels). Most existing me…
Geometry Guided Adversarial Facial Expression Synthesis
Lingxiao Song, Zhihe Lu, Ran He +2
Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However,…
Recent Progress of Face Image Synthesis
Zhihe Lu, Zhihang Li, Jie Cao +2
Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic…