Search Results for author:
Found 9 papers, 2 papers with code
Bi-Level Meta-Learning for Few-Shot Domain Generalization
We address FSDG problem by meta-learning two levels of meta-knowledge, where the lower-level meta-knowledge are domain-specific embedding spaces as subspaces of a base space for intra-domain generalization, and the upper-level meta-knowledge is the base space and a prior subspace over domain-specific spaces for inter-domain generalization.
Layout-Based Causal Inference for Object Navigation
The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor.
See More for Scene: Pairwise Consistency Learning for Scene Classification
In this paper, we propose to understand scene images and the scene classification CNN models in terms of the focus area.
Hierarchical Object-to-Zone Graph for Object Navigation
In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments.
Dataset Bias in Few-shot Image Recognition
Second, we investigate performance differences on different datasets from dataset structures and different few-shot learning methods.
Scene Recognition with Prototype-agnostic Scene Layout
Due to the large intra-class structural diversity, building and modeling flexible structural layout to adapt various image characteristics is a challenge.
Learning Effective RGB-D Representations for Scene Recognition
We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scene.
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features.
Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold
An important advantage of modeling features in a semantic space, is that this space is feature independent.
