19 citations · 22 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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.…