23 citations · 71 across the 20 of their papers we have counts for
5 papers · 1 filter
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Zelin Peng, Zhengqin Xu, Zhilin Zeng +2
Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downst…
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
Yichen Xie, Han Lu, Junchi Yan +3
Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the…
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Chao Chen, Haoyu Geng, Nianzu Yang +2
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to d…
Xformer: Hybrid X-Shaped Transformer for Image Denoising
Jiale Zhang, Yulun Zhang, Jinjin Gu +3
In this paper, we present a hybrid X-shaped vision Transformer, named Xformer, which performs notably on image denoising tasks. We explore strengthening the global representation o…
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Chao Chen, Haoyu Geng, Gang Zeng +4
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only…