Search Results for author:
Found 7 papers, 5 papers with code
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance
We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range.
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation
GEA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation.
Ranked #16 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
Guided Evolution for Neural Architecture Search
The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation.
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).
Ranked #1 on
Human action generation
on Human3.6M
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
It is known that the kinematics of the human body skeleton reveals valuable information in action recognition.
Iterative weak/self-supervised classification framework for abnormal events detection
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Ranked #1 on
Semi-supervised Anomaly Detection
on UBI-Fights
Weakly and Partially Supervised Learning Frameworks for Anomaly Detection
The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns.
Ranked #1 on
Abnormal Event Detection In Video
on UBI-Fights
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+2
