78 citations · 91 across the 6 of their papers we have counts for
8 papers
Transformers in Single Object Tracking: An Experimental Survey
Janani Thangavel, Thanikasalam Kokul, Amirthalingam Ramanan +1
Single-object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve…
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +4
Any clustering algorithm must synchronously learn to model the clusters and allocate data to those clusters in the absence of labels. Mixture model-based methods model clusters wit…
End-To-End Data-Dependent Routing in Multi-Path Neural Networks
Dumindu Tissera, Rukshan Wijessinghe, Kasun Vithanage +3
Neural networks are known to give better performance with increased depth due to their ability to learn more abstract features. Although the deepening of networks has been well est…
Transferring Domain Knowledge with an Adviser in Continuous Tasks
Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera +3
Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapa…
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +3
Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks…
Context-Aware Multipath Networks
Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe +2
Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized i…
Wavelet based edge feature enhancement for convolutional neural networks
D. D. N. De Silva, S. Fernando, I. T. S. Piyatilake +1
Convolutional neural networks are able to perform a hierarchical learning process starting with local features. However, a limited attention is paid to enhancing such elementary le…
On Optimizing Deep Convolutional Neural Networks by Evolutionary Computing
M. U. B. Dias, D. D. N. De Silva, S. Fernando
Optimization for deep networks is currently a very active area of research. As neural networks become deeper, the ability in manually optimizing the network becomes harder. Mini-ba…