The One About Computer Vision (Round II)
From representing complex biological shapes to curating large-scale vision datasets, advances in computer vision are redefining how AI learns from visual information. Hear from researchers as they share new techniques for improving visual representations, increasing data efficiency, and building more capable computer vision systems.
More information to come soon, stay tuned! 👀
More About The Sharings
Jagadish (Senior Scientist, A*STAR) will share on "Biological Shape as Data: From Contours to Conclusions"
Shape is everywhere in biology, but turning it into something a machine can analyse reliably is harder than it looks. Jagadish will introduce a shape embedding approach designed to be fast, accurate, and lightweight, while remaining invariant to the position and rotation of objects. He'll walk through how it holds up against other methods including machine learning approaches in benchmark tests, before exploring two applications of the method to understanding biological phenomena in cancer. (Techincal Level: 100)
Hu Zhi (Researcher, KLASS Engineering and Solutions Pte Ltd) will share on "Distribution-Aware Curation for Semantic Segmentation" (as part of the “Physical AI & Robotics Sharing Series by KLASS”)
Semantic segmentation depends on dense pixel-level annotations, making large-scale dataset construction costly and difficult to scale. Hu Zhi will explore data curation as an efficient alternative to indiscriminate annotation by selecting a compact yet representative subset from a raw image pool. The talk will introduce a distribution-aware curation framework that models class-wise feature distributions, producing a compact yet representative training set for semantic segmentation. He will also discuss future extensions toward multi-modal data curation for large-scale data-driven AI, including VLMs and VLAs, where distributionally representative data can improve data efficiency and reduce training costs. (Technical Level: 300)
More About The Speakers
Dr. Jagadish Sankaran is a Senior Scientist I at A*STAR, Singapore, with expertise in big data, biomedical data analytics, fluorescence imaging, image analysis, and biological discovery. He received his Ph. D in Computational and Systems Biology. Building upon his foundation in microscopy, he has transitioned into image processing and integrating morphological data with genomics. He received the Next Gen Leadership Award in Genomics at the Advances in Genome Biology and Technology (AGBT) conference in Orlando, USA in 2024. He has spoken about his research at many forums, including the Gordon Research Seminars, the Keystone Meetings, Data Innovation Summit, CDAO and the Global Engage meeting series.
Dr. Hu Zhi is currently working as a researcher at KLASS Engineering and Solutions Pte Ltd. He received his degree in Electrical Engineering with Highest Honours from NUS and pursued his Ph.D. in Computer Science at NTU under the supervision of Prof. Lin Weisi. His experience and expertise focuses on spatial intelligence and embodied intelligence, including navigation, localization, scene understanding, and intelligent agent interaction.
More About The Series
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