283 citations · 546 across the 36 of their papers we have counts for
39 papers
CART: Compatible Routing for Multimodal Continual Instruction Tuning
Ling-Xiang Zhang, Tao Hu, Da-Wei Zhou
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world applications often require them to continually acquire new capabiliti…
AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
Zhen-Hao Xie, Yu-Cheng Shi, Da-Wei Zhou
Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing similarity between visual a…
Prism: A Plug-in Reproducible Infrastructure for Scalable Multimodal Continual Instruction Tuning
Jun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie +1
Multimodal Large Language Models (MLLMs) achieve versatility by reformulating diverse tasks into a unified instruction-following framework via instruction tuning. However, real-wor…
Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
Jiayu Li, Enpei Zhang, Dawei Zhou +2
We study workflow learning in a setting where specialized agents hand off control through a shared artifact, each agent observes only a local function of that artifact and its own…
Stable Routing for Mixture-of-Experts in Class-Incremental Learning
Zirui Guo, Quan Cheng, Da-Wei Zhou +1
Class-incremental learning (CIL) requires models to learn new classes sequentially while preserving prior knowledge. Recently, approaches that combine pre-trained models with mixtu…
Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
Hao Sun, Zi-Jun Ding, Da-Wei Zhou
Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkable generalization of CLIP, l…
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
Tao Hu, Da-Wei Zhou
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual capability expansion across seque…
Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
Zhen-Hao Xie, Yan Wang, Hao Sun +3
Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world scenarios such as autonomous d…
Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
Yizhi Zhou, Jia-Qi Yang, De-Chuan Zhan +1
Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on…
SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
Zhen-Hao Xie, Jun-Tao Tang, Yu-Cheng Shi +3
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually expand their capabilities, ma…
HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
Zhen-Hao Xie, Yan Wang, Lan Li +3
Class-Incremental Learning (CIL) aims to endow models with the ability to continuously adapt to evolving data streams. Recent advances in pre-trained vision-language models (e.g.,…
The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?
Guannan Lai, Da-Wei Zhou, Xin Yang +1
Class Incremental Learning (CIL) requires models to continuously learn new classes without forgetting previously learned ones, while maintaining stable performance across all possi…
Integrating Task-Specific and Universal Adapters for Pre-Trained Model-based Class-Incremental Learning
Yan Wang, Da-Wei Zhou, Han-Jia Ye
Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Existing pre-trained model-based CIL methods often freeze the pre-t…
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
Lan Li, Da-Wei Zhou, Han-Jia Ye +1
Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge…
Task-Agnostic Guided Feature Expansion for Class-Incremental Learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye +1
The ability to learn new concepts while preserve the learned knowledge is desirable for learning systems in Class-Incremental Learning (CIL). Recently, feature expansion of the mod…
External Knowledge Injection for CLIP-Based Class-Incremental Learning
Da-Wei Zhou, Kai-Wen Li, Jingyi Ning +3
Class-Incremental Learning (CIL) enables learning systems to continuously adapt to evolving data streams. With the advancement of pre-training, leveraging pre-trained vision-langua…
MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning
Hai-Long Sun, Da-Wei Zhou, Hanbin Zhao +3
Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones. Despite Pre-trained Models (PTMs) have shown excellent…
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning
Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye +2
Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. While recent advances in pre-trained models provide a sol…
Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
Zhi-Hong Qi, Da-Wei Zhou, Yiran Yao +2
In our ever-evolving world, new data exhibits a long-tailed distribution, such as e-commerce platform reviews. This necessitates continuous model learning imbalanced data without f…
LW2G: Learning Whether to Grow for Prompt-based Continual Learning
Qian Feng, Da-wei Zhou, Hanbin Zhao +4
Recent Prompt-based Continual learning (PCL) has achieved remarkable performance with pre-trained models. These approaches expand a prompt pool by adding a new set of prompts while…
Parrot: Multilingual Visual Instruction Tuning
Hai-Long Sun, Da-Wei Zhou, Yang Li +8
The rapid development of Multimodal Large Language Models (MLLMs), such as GPT-4o, marks a significant step toward artificial general intelligence. Existing methods typically align…
TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen
Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye +1
The era of pre-trained models has ushered in a wealth of new insights for the machine learning community. Among the myriad of questions that arise, one of paramount importance is:…
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye +1
Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Despite the strong performance of Pre-Trained Models (PTMs) in CIL,…
Continual Learning with Pre-Trained Models: A Survey
Da-Wei Zhou, Hai-Long Sun, Jingyi Ning +2
Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve thi…