202 citations · 507 across the 33 of their papers we have counts for
4 papers · 1 filter
Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning
Jiang-Tian Zhai, Xialei Liu, Lu Yu +1
Non-exemplar class incremental learning aims to learn both the new and old tasks without accessing any training data from the past. This strict restriction enlarges the difficulty…
Class Incremental Learning with Pre-trained Vision-Language Models
Xialei Liu, Xusheng Cao, Haori Lu +3
With the advent of large-scale pre-trained models, interest in adapting and exploiting them for continual learning scenarios has grown. In this paper, we propose an approach to exp…
Masked Autoencoders are Efficient Class Incremental Learners
Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov +2
Class Incremental Learning (CIL) aims to sequentially learn new classes while avoiding catastrophic forgetting of previous knowledge. We propose to use Masked Autoencoders (MAEs) a…
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
Xin Jin, Jia-Wen Xiao, Ling-Hao Han +4
Explicit calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods are impeded by several critical limitations: a)…