Search Results for author:
Found 8 papers, 7 papers with code
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
Graph Transformer
Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear.
Learning Continuous Image Representation with Local Implicit Image Function
How to represent an image?
Ranked #2 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(SSIM metric)
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate.
Space-time Video Super-resolution
Video Frame Interpolation
+1
Learning Implicit Feature Alignment Function for Semantic Segmentation
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Transformers as Meta-Learners for Implicit Neural Representations
Motivated by a generalized formulation of gradient-based meta-learning, we propose a formulation that uses Transformers as hypernetworks for INRs, where it can directly build the whole set of INR weights with Transformers specialized as set-to-set mapping.
Visual Reinforcement Learning with Self-Supervised 3D Representations
A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved sample-efficiency and generalization through additional learning signal and inductive biases.
