5 citations · 6 across the 6 of their papers we have counts for
6 papers
Composition-Incremental Learning for Compositional Generalization
Zhen Li, Yuwei Wu, Chenchen Jing +3
Compositional generalization has achieved substantial progress in computer vision on pre-collected training data. Nonetheless, real-world data continually emerges, with possible co…
Adaptive Model Ensemble for Continual Learning
Yuchuan Mao, Zhi Gao, Xiaomeng Fan +3
Model ensemble is an effective strategy in continual learning, which alleviates catastrophic forgetting by interpolating model parameters, achieving knowledge fusion learned from d…
Consistency of Compositional Generalization across Multiple Levels
Chuanhao Li, Zhen Li, Chenchen Jing +4
Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phra…
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
Chuanhao Li, Zhen Li, Chenchen Jing +6
Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources…
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
Muzhi Zhu, Hengtao Li, Hao Chen +5
Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel object…
Learning Conditional Attributes for Compositional Zero-Shot Learning
Qingsheng Wang, Lingqiao Liu, Chenchen Jing +4
Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the c…