6 papers
WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG
Zhen Li, Zian Meng, Shuwei Shi +5
Dynamical systems theory and reinforcement learning view world evolution as latent-state dynamics driven by actions, with visual observations providing partial information about th…
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…
Multi-Step Reasoning for Embodied Question Answering via Tool Augmentation
Mingliang Zhai, Hansheng Liang, Xiaomeng Fan +6
Embodied Question Answering (EQA) requires agents to explore 3D environments to obtain observations and answer questions related to the scene. Existing methods leverage VLMs to dir…
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…
Multi-Sourced Compositional Generalization in Visual Question Answering
Chuanhao Li, Wenbo Ye, Zhen Li +2
Compositional generalization is the ability of generalizing novel compositions from seen primitives, and has received much attention in vision-and-language (V\&L) recently. Due to…
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…