Search Results for author:
Found 10 papers, 5 papers with code
Few-shot Domain-Adaptive Visually-fused Event Detection from Text
In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points.
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain
In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i. e., in the form of probabilistic knowledge graphs).
Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.
MARLIN: Masked Autoencoder for facial video Representation LearnINg
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS).
Ranked #1 on
Emotion Classification
on CMU-MOSEI
Paraphrasing Techniques for Maritime QA system
In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.
Active Learning by Feature Mixing
We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations.
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation.
Ranked #22 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.
