25 citations · 50 across the 13 of their papers we have counts for
8 papers · 1 filter
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
Kun Ding, Haojian Zhang, Qiang Yu +3
We propose a generalized method for boosting the generalization ability of pre-trained vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is rea…
Reusable Architecture Growth for Continual Stereo Matching
Chenghao Zhang, Gaofeng Meng, Bin Fan +4
The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks…
Enhancing Visual Continual Learning with Language-Guided Supervision
Bolin Ni, Hongbo Zhao, Chenghao Zhang +4
Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures,…
Defying Imbalanced Forgetting in Class Incremental Learning
Shixiong Xu, Gaofeng Meng, Xing Nie +3
We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incr…
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
Guangliang Cheng, Yunmeng Huang, Xiangtai Li +4
Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has bro…
Pro-tuning: Unified Prompt Tuning for Vision Tasks
Xing Nie, Bolin Ni, Jianlong Chang +6
In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks. However, deploying it in practice is quite challenging,…