16 citations · 21 across the 12 of their papers we have counts for
13 papers
Type-Balanced Contextual Learning for Incremental Named Entity Recognition
Duzhen Zhang, Yahan Yu, Xiuyi Chen +2
Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity types within unstructured te…
Progressive Multimodal Alignment for Continual Instruction Tuning
Duzhen Zhang, Yahan Yu, Qiaoyi Su +2
Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cross-modal understanding. In M…
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
Jiahua Dong, Wenqi Liang, Hongliu Li +7
Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts. However, the majority of CDMs unrealistic…
Bring Your Dreams to Life: Continual Text-to-Video Customization
Jiahua Dong, Xudong Wang, Wenqi Liang +7
Customized text-to-video generation (CTVG) has recently witnessed great progress in generating tailored videos from user-specific text. However, most CTVG methods assume that perso…
Exploring Stability-Plasticity Trade-offs for Continual Named Entity Recognition
Duzhen Zhang, Chenxing Li, Jiahua Dong +2
Continual Named Entity Recognition (CNER) is an evolving field that focuses on sequentially updating an existing model to incorporate new entity types. Previous CNER methods primar…
Enhancing Multimodal Continual Instruction Tuning with BranchLoRA
Duzhen Zhang, Yong Ren, Zhong-Zhi Li +5
Multimodal Continual Instruction Tuning (MCIT) aims to finetune Multimodal Large Language Models (MLLMs) to continually align with human intent across sequential tasks. Existing ap…
Hearing from Silence: Reasoning Audio Descriptions from Silent Videos via Vision-Language Model
Yong Ren, Chenxing Li, Le Xu +7
Humans can intuitively infer sounds from silent videos, but whether multimodal large language models can perform modal-mismatch reasoning without accessing target modalities remain…
Lifelong Learning of Large Language Model based Agents: A Roadmap
Junhao Zheng, Chengming Shi, Xidi Cai +5
Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously a…
Federated Incremental Named Entity Recognition
Duzhen Zhang, Yahan Yu, Chenxing Li +2
Federated Named Entity Recognition (FNER) boosts model training within each local client by aggregating the model updates of decentralized local clients, without sharing their priv…
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Jiahua Dong, Wenqi Liang, Hongliu Li +5
Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably a…
Prompt-guided Precise Audio Editing with Diffusion Models
Manjie Xu, Chenxing Li, Duzhen zhang +3
Audio editing involves the arbitrary manipulation of audio content through precise control. Although text-guided diffusion models have made significant advancements in text-to-audi…
MM-LLMs: Recent Advances in MultiModal Large Language Models
Duzhen Zhang, Yahan Yu, Jiahua Dong +4
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effecti…
Continual Named Entity Recognition without Catastrophic Forgetting
Duzhen Zhang, Wei Cong, Jiahua Dong +4
Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating an existing model by incorporating new entity types sequentially. Nevertheless, continual le…