AI for


Our Research


Research is at our core. As an AI technology company, being involved in both theoretical and applied research keeps us at the bleeding edge. We conduct research in multiple related areas such as machine learning, deep learning and computer vision by utilizing our core team as well in collaboration with academics from world’s top universities such as University of Cambridge.



AI for Mitigating Effects of Climate and Weather Changes in Agriculture

Narmada Balasooriya, C.D. Athuraliya, Janak Gunatilleke

In recent years, floods, landslides and droughts have become an annual occurrence in Sri Lanka. Despite the efforts made by the government and other entities, these natural disasters remain challenging mainly to the people who live in high risk areas. It is also crucial to predict such disasters early on to facilitate evacuation of people living in these areas. Furthermore, Sri Lankan economy largely depends on agriculture, yet this sector still remains untouched by recent advancements of AI and other predictive analytics techniques. There is an increased tendency amongst Sri Lankan youth to refrain from agriculture sector due to the lack of technology adoption and risks associated with it. The work by Peiris et.al demonstrates how seasonal climate data is used to predict coconut yield in Sri Lanka. Another Sri Lankan tech company has initiated the project AiGrow to increase the use of state-of-the-art technology in agriculture sector by utilizing AI, smart sensor systems and fertigation systems (automated fertilizer delivery machine). Regionally, Pulse Lab Jakarta has developed a visualization tool to track the impact of forest and peatland fires in Indonesia.

International Conference on Learning Representations (ICLR) 2019 AI for Social Good WorkshopPDF



A Context-aware Capsule Network for Multi-label Classification

Sameera Ramasinghe, C.D. Athuraliya, Salman H. Khan

CapsNet architecture

Recently proposed Capsule Network is a brain inspired architecture that brings a new paradigm to deep learning by modelling input domain variations through vector based representations. Despite being a seminal contribution, CapsNet does not explicitly model structured relationships between the detected entities and among the capsule features for related inputs. Motivated by the working of cortical network in human visual system, we seek to resolve CapsNet limitations by proposing several intuitive modifications to the CapsNet architecture. We introduce, (1) a novel routing weight initialization technique, (2) an improved CapsNet design that exploits semantic relationships between the primary capsule activations using a densely connected Conditional Random Field and (3) a Cholesky transformation based correlation module to learn a general priority scheme. Our proposed design allows CapsNet to scale better to more complex problems, such as the multi-label classification task, where semantically related categories co-exist with various interdependencies. We present theoretical bases for our extensions and demonstrate significant improvements on ADE20K scene dataset.

European Conference on Computer Vision (ECCV) 2018 workshop on Brain-Driven Computer VisionSpringer, arXiv



Also check our previous research on Google Scholar.