6 papers
Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
Linjie Li, Huiyu Xiao, Jiarui Cao +2
Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all seen classes. Although pretrained mode…
LDEPrompt: Layer-importance guided Dual Expandable Prompt Pool for Pre-trained Model-based Class-Incremental Learning
Linjie Li, Zhenyu Wu, Huiyu Xiao +1
Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-level matching to select the mos…
UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos
Zhi Yang, Lingfeng Zeng, Fangqi Lou +16
Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-den…
MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
Linjie Li, Zhenyu Wu, Yang Ji
Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information. Recently,…
EmoAssist: Emotional Assistant for Visual Impairment Community
Xingyu Qi, He Li, Linjie Li +1
The rapid advancement of large multi-modality models (LMMs) has significantly propelled the integration of artificial intelligence into practical applications. Visual Question Answ…
TaE: Task-aware Expandable Representation for Long Tail Class Incremental Learning
Linjie Li, Zhenyu Wu, Jiaming Liu +1
Class-incremental learning is dedicated to the development of deep learning models that are capable of acquiring new knowledge while retaining previously learned information. Most…