19 citations · 22 across the 12 of their papers we have counts for
12 papers
LLaVA-c: Continual Improved Visual Instruction Tuning
Wenzhuo Liu, Fei Zhu, Haiyang Guo +2
Multimodal models like LLaVA-1.5 achieve state-of-the-art visual understanding through visual instruction tuning on multitask datasets, enabling strong instruction-following and mu…
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond
Haiyang Guo, Fanhu Zeng, Fei Zhu +9
The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specifi…
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery
Shijie Ma, Fei Zhu, Xu-Yao Zhang +1
Generalized category discovery (GCD) is a pragmatic but underexplored problem, which requires models to automatically cluster and discover novel categories by leveraging the labele…
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model
Haiyang Guo, Fanhu Zeng, Ziwei Xiang +4
Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of…
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu +5
A vast amount of instruction tuning data is crucial for the impressive performance of Large Multimodal Models (LMMs), but the associated computational costs and data collection dem…
Happy: A Debiased Learning Framework for Continual Generalized Category Discovery
Shijie Ma, Fei Zhu, Zhun Zhong +3
Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aim…
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt
Fanhu Zeng, Fei Zhu, Haiyang Guo +2
Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed instruction datasets. However, novel tasks would be encountered sequentially in dynamic wo…
WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Models
Xinjian Wu, Ruisong Zhang, Jie Qin +2
Segmenting and recognizing diverse object parts is crucial in computer vision and robotics. Despite significant progress in object segmentation, part-level segmentation remains und…
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution
Wenzhuo Liu, Fei Zhu, Shijie Ma +1
Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutio…
Multi-scale Unified Network for Image Classification
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition. However, they face notable challenges in performance and computa…
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Deep neural networks perform remarkably well in close-world scenarios. However, novel classes emerged continually in real applications, making it necessary to learn incrementally.…
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications co…