4 citations · 13 across the 14 of their papers we have counts for
14 papers
Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning
Zhiming Xu, Huiyu Yi, Zhen-Hao Xie +4
Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks. However, adapti…
Beyond Routing Saturation: A Long-Horizon Class-Incremental Perspective on Expert Routing in Multimodal Continual Instruction Tuning
Huiyu Yi, Yongqi Xu, Bogang Zhang +5
Multimodal Continual Instruction Tuning (MCIT) enables multimodal large language models to acquire new tasks sequentially while retaining previously learned capabilities. Many rece…
Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning
Huiyu Yi, Zhiming Xu, Dunwei Tu +3
The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting compared to Fully Connected lay…
Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals
Zhiming Xu, Baile Xu, Jian Zhao +2
Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplor…
Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Suorong Yang, Fangjian Su, Hai Gan +5
Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted…
Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins
Zhiming Xu, Baile Xu, Jian Zhao +2
Continual learning requires models to learn continuously while preserving prior knowledge under evolving data streams. Distillation-based methods are appealing for retaining past k…
Physics-inspired Energy Transition Neural Network for Sequence Learning
Zhou Wu, Junyi An, Baile Xu +2
Recently, the superior performance of Transformers has made them a more robust and scalable solution for sequence modeling than traditional recurrent neural networks (RNNs). Howeve…
Interactive Instance Annotation with Siamese Networks
Xiang Xu, Ruotong Li, Mengjun Yi +3
Annotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. Howeve…
Multiple Queries with Multiple Keys: A Precise Prompt Matching Paradigm for Prompt-based Continual Learning
Dunwei Tu, Huiyu Yi, Yuchi Wang +3
Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based…
Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning
Zhiming Xu, Suorong Yang, Baile Xu +2
Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly f…
Faster and Simpler Siamese Network for Single Object Tracking
Shaokui Jiang, Baile Xu, Jian Zhao +1
Single object tracking (SOT) is currently one of the most important tasks in computer vision. With the development of the deep network and the release for a series of large scale d…
Super Interaction Neural Network
Yang Yao, Xu Zhang, Baile Xu +2
Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features. However, in lightweight networks, there are limited…
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
Xu Zhang, Yang Yao, Baile Xu +4
Class incremental learning refers to a special multi-class classification task, in which the number of classes is not fixed but is increasing with the continual arrival of new data…
Operation-aware Neural Networks for User Response Prediction
Yi Yang, Baile Xu, Furao Shen +1
User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactio…