61 citations · 337 across the 72 of their papers we have counts for
14 papers · 1 filter
MAGIC: Achieving Superior Model Merging via Magnitude Calibration
Yayuan Li, Jian Zhang, Jintao Guo +4
The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised…
On the Implicit Adversariality of Catastrophic Forgetting in Deep Continual Learning
Ze Peng, Jian Zhang, Jintao Guo +3
Continual learning seeks the human-like ability to accumulate new skills in machine intelligence. Its central challenge is catastrophic forgetting, whose underlying cause has not b…
An Adaptor for Triggering Semi-Supervised Learning to Out-of-Box Serve Deep Image Clustering
Yue Duan, Lei Qi, Yinghuan Shi +1
Recently, some works integrate SSL techniques into deep clustering frameworks to enhance image clustering performance. However, they all need pretraining, clustering learning, or a…
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi +3
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled da…
Towards Perfection: Building Inter-component Mutual Correction for Retinex-based Low-light Image Enhancement
Luyang Cao, Han Xu, Jian Zhang +4
In low-light image enhancement, Retinex-based deep learning methods have garnered significant attention due to their exceptional interpretability. These methods decompose images in…
Correspondence as Video: Test-Time Adaption on SAM2 for Reference Segmentation in the Wild
Haoran Wang, Zekun Li, Jian Zhang +2
Large vision models like the Segment Anything Model (SAM) exhibit significant limitations when applied to downstream tasks in the wild. Consequently, reference segmentation, which…