3 citations · 5 across the 8 of their papers we have counts for
8 papers
Predictive Regularization Against Visual Representation Degradation in Multimodal Large Language Models
Enguang Wang, Qiang Wang, Yuanchen Wu +5
While Multimodal Large Language Models (MLLMs) excel at vision-language tasks, the cost of their language-driven training on internal visual foundational competence remains unclear…
Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery
Zhimao Peng, Enguang Wang, Fei Yang +2
Generalized category discovery (GCD) is an important and challenging task in open-world learning. Specifically, given some labeled data of known classes, GCD aims to cluster unlabe…
Predictive Sample Assignment for Semantically Coherent Out-of-Distribution Detection
Zhimao Peng, Enguang Wang, Xialei Liu +1
Semantically coherent out-of-distribution detection (SCOOD) is a recently proposed realistic OOD detection setting: given labeled in-distribution (ID) data and mixed in-distributio…
Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning
Xusheng Cao, Haori Lu, Linlan Huang +3
Continual learning in computer vision faces the critical challenge of catastrophic forgetting, where models struggle to retain prior knowledge while adapting to new tasks. Although…
Generative Multi-modal Models are Good Class-Incremental Learners
Xusheng Cao, Haori Lu, Linlan Huang +2
In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant chall…
GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery
Enguang Wang, Zhimao Peng, Zhengyuan Xie +3
Given unlabelled datasets containing both old and new categories, generalized category discovery (GCD) aims to accurately discover new classes while correctly classifying old class…
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…