2 citations · 4 across the 18 of their papers we have counts for
18 papers
Denser Better: Limits of On-Policy Self-Distillation for Continual Post-Training
Meng Wang, Haohan Zhao, Wenzhuo Liu +7
Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgett…
CL-VISTA: Benchmarking Continual Learning in Video Large Language Models
Haiyang Guo, Yichen Shi, Fei Zhu +6
Video Large Language Models (Video-LLMs) require continual learning to adapt to non-stationary real-world data. However, existing benchmarks fall short of evaluating modern foundat…
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
Hongbo Zhao, Meng Wang, Fei Zhu +5
The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC)…
C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Ming-Ming Yu, Fei Zhu, Wenzhuo Liu +4
Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of…
MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark
Haiyang Guo, Fei Zhu, Hongbo Zhao +5
Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Mu…
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Song Lai, Haohan Zhao, Rong Feng +9
Continual post-training (CPT) is a popular and effective technique for adapting foundation models like multimodal large language models to ever-evolving downstream tasks. While exi…
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…
Global Convergence of Continual Learning on Non-IID Data
Fei Zhu, Yujing Liu, Wenzhuo Liu +1
Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical a…
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…
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
Yi Chen, Jian Xu, Xu-Yao Zhang +3
With the advancement of large-scale language modeling techniques, large multimodal models combining visual encoders with large language models have demonstrated exceptional perform…
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…
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning
Haiyang Guo, Fei Zhu, Wenzhuo Liu +2
Existing federated learning methods have effectively dealt with decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each…
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning
Wenzhuo Liu, Xinjian Wu, Fei Zhu +3
Deep Neural Network (DNN) has achieved great success on datasets of closed class set. However, new classes, like new categories of social media topics, are continuously added to th…