5 accepted papers
Deep reinforcement learning (DRL) typically requires reinitializing training for new tasks, limiting its generalization due to isolated knowledge transfer. Meta-reinforcement learning (Meta-RL) addresses this by enabling rapid adaptation through prior task experiences, yet existing gradient-based me
We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly objects in urban aerial images exhibit substantial variations in size inc…
Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However MLLMs still face a fundamental limitation of hallucinations where they tend to generate erroneous or fabricated information. In this paper…
Meta-Reinforcement Learning (Meta-RL) is a machine learning paradigm aimed at learning reinforcement learning policies that can quickly adapt to unseen tasks with few-shot data. Nevertheless, applying Meta-RL to real-world applications faces challenges due to the cost of data acquisition. To address…
It is well acknowledged that person re-identification (person ReID) highly relies on visual texture information like clothing. Despite significant progress has been made in recent years, texture-confusing situations like clothing changing and persons wearing the same clothes receive little attention…