13 citations · 16 across the 28 of their papers we have counts for
12 papers · 1 filter
ReCAP: Retrieval-Guided Capability Reuse for Multimodal Continual Instruction Tuning
Tao Hu, Zhinuo Zhou, Xialiang Tong +2
Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks while preserving previously learned…
Visual Branch is What You Need for CLIP-based Class-Incremental Learning
Tao Hu, Zhen-Hao Xie, Jingcai Guo +2
Class-Incremental Learning (CIL) requires models to recognize new classes over time without forgetting previously learned ones. With the rise of vision-language pre-training, CLIP…
Polaris: Scaling Up Instruction-Guided Image Generation Towards Millions of Personalized Style Needs
Zhi-Kai Chen, Jun-Peng Jiang, Jun-Jie Tao +2
Users increasingly expect image generation models to quickly adapt to highly diverse and personalized requirements, such as producing images with distinctive styles or characterist…
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…
BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
Lan Li, Tao Hu, Da-Wei Zhou +3
Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models such as CLIP offer strong transfe…
Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation
Zhi-Kai Chen, Jun-Peng Jiang, Han-Jia Ye +1
Autoregressive (AR) image generation models are capable of producing high-fidelity images but often suffer from slow inference due to their inherently sequential, token-by-token de…
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.,…
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…
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…
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
Chao Yi, Lu Ren, De-Chuan Zhan +1
CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scena…
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
Bowen Tao, Lan Li, Xin-Chun Li +1
Due to the advantages of leveraging unlabeled data and learning meaningful representations, semi-supervised learning and contrastive learning have been progressively combined to ac…