64 citations · 157 across the 6 of their papers we have counts for
7 papers · 1 filter
Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning
Sixian Zhang, Yiyao Wang, Xinhang Song +3
Understanding the geometric and semantic structure of environments is essential for embodied navigation and reasoning. Existing semantic mapping methods trade off between explicit…
TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation
Yiyao Wang, Sixian Zhang, Keming Zhang +3
Existing zero-shot Object Goal Navigation (ObjectNav) methods often exploit commonsense knowledge from large language or vision-language models to guide navigation. However, such k…
Hierarchical Object-to-Zone Graph for Object Navigation
Sixian Zhang, Xinhang Song, Yubing Bai +3
The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an…
Dataset Bias in Few-shot Image Recognition
Shuqiang Jiang, Yaohui Zhu, Chenlong Liu +3
The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base…
Scene Recognition with Prototype-agnostic Scene Layout
Gongwei Chen, Xinhang Song, Haitao Zeng +1
Abstract--- Exploiting the spatial structure in scene images is a key research direction for scene recognition. Due to the large intra-class structural diversity, building and mode…
Learning Effective RGB-D Representations for Scene Recognition
Xinhang Song, Shuqiang Jiang, Luis Herranz +1
Deep convolutional networks (CNN) can achieve impressive results on RGB scene recognition thanks to large datasets such as Places. In contrast, RGB-D scene recognition is still und…
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
Xinhang Song, Luis Herranz, Shuqiang Jiang
Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene dat…