title: Image classification using class-agnostic object detection

authors: Holmes, Geoffrey

subjects: human-in-the-loop machine learning

abstract: Human-in-the-loop interfaces for machine learning provide a promising way to reduce the annotation effort required to obtain an accurate machine learning model, particularly when it is used with transfer learning to exploit existing knowledge gleaned from another domain. This paper explores the use of a human-in-the-loop strategy that is designed to build a deep-learning image classification model iteratively using successive batches of images that the user labels. Specifically, we examine whether class-agnostic object detection can improve performance by providing a focus area for image classification in the form of a bounding box. The goal is to reduce the amount of effort required to label a batch of images by presenting the user with the current predictions of the model on a new batch of data and only requiring correction of those predictions. User effort is measured in terms of the number of corrections made. Results show that the use of bounding boxes always leads to fewer corrections. The benefit of a bounding box is that it also provides feedback to the user because it indicates whether or not the classification of the deep learning model is based on the appropriate part of the image. This has implications for the design of user interfaces in this application scenario.

text: Image classification using class-agnostic object detection Human-in-the-loop interfaces for machine learning provide a promising way to reduce the annotation effort required to obtain an accurate machine learning model, particularly when it is used with transfer learning to exploit existing knowledge gleaned from another domain. This paper explores the use of a human-in-the-loop strategy that is designed to build a deep-learning image classification model iteratively using successive batches of images that the user labels. Specifically, we examine whether class-agnostic object detection can improve performance by providing a focus area for image classification in the form of a bounding box. The goal is to reduce the amount of effort required to label a batch of images by presenting the user with the current predictions of the model on a new batch of data and only requiring correction of those predictions. User effort is measured in terms of the number of corrections made. Results show that the use of bounding boxes always leads to fewer corrections. The benefit of a bounding box is that it also provides feedback to the user because it indicates whether or not the classification of the deep learning model is based on the appropriate part of the image. This has implications for the design of user interfaces in this application scenario.

year: 2023

title: Statement of Evidence (Coastal impacts)

authors: de Lange, Willem P.

abstract: Evidence presented to Consent Hearing on Proposed Coastal Protection Works at Wainui Beach

text: Statement of Evidence (Coastal impacts) Evidence presented to Consent Hearing on Proposed Coastal Protection Works at Wainui Beach

year: 2018

title: Addressing disparities by listening to Māori whānau: Lessons for Kāhui Ako

authors: Horne, Sharon Karey

subjects: Addressing

abstract: This research involved interviewing five Māori whānau, with children attending four of six schools across a  Kāhui Ako, to identify the extent to which their children were enjoying and achieving education success as Māori.
A review of the literature highlighted precolonial ways of teaching and learning where whānau were highly involved in the education of their children and where teaching and learning was reciprocal and where the mana of children, whānau and hapū were important.
Applying culturally responsive methodologies and using group focussed, semi-structured interviews to collect whānau perspectives it was revealed that many Māori children were neither enjoying education nor achieving success as Māori. Māori whānau spoke sincerely about their own experiences and those of their children.  Critical and consistent messages from Māori whānau identified what must be addressed if Māori children are to reach their full potential and enjoy and achieve education success with their Māori cultural identity secure. 
This thesis identifies that it is crucial for school leaders and teachers to listen to what whānau are saying if educators are to take greater responsibility for the performance of Māori students’ learning and cultural identity by delivering their teaching performance against the cultural competencies stated in Tātaiako.  Addressing, reducing and removing the ongoing challenges and disparities Māori children and whānau face in education is essential. However, unless school leaders and teachers believe they have to change, then Māori will continue to get from schooling what they have always got. That is not good enough for Māori whānau and nor is it good enough for anyone’s whānau. This thesis discusses how together we can make this happen.

text: Addressing disparities by listening to Māori whānau: Lessons for Kāhui Ako This research involved interviewing five Māori whānau, with children attending four of six schools across a  Kāhui Ako, to identify the extent to which their children were enjoying and achieving education success as Māori.
A review of the literature highlighted precolonial ways of teaching and learning where whānau were highly involved in the education of their children and where teaching and learning was reciprocal and where the mana of children, whānau and hapū were important.
Applying culturally responsive methodologies and using group focussed, semi-structured interviews to collect whānau perspectives it was revealed that many Māori children were neither enjoying education nor achieving success as Māori. Māori whānau spoke sincerely about their own experiences and those of their children.  Critical and consistent messages from Māori whānau identified what must be addressed if Māori children are to reach their full potential and enjoy and achieve education success with their Māori cultural identity secure. 
This thesis identifies that it is crucial for school leaders and teachers to listen to what whānau are saying if educators are to take greater responsibility for the performance of Māori students’ learning and cultural identity by delivering their teaching performance against the cultural competencies stated in Tātaiako.  Addressing, reducing and removing the ongoing challenges and disparities Māori children and whānau face in education is essential. However, unless school leaders and teachers believe they have to change, then Māori will continue to get from schooling what they have always got. That is not good enough for Māori whānau and nor is it good enough for anyone’s whānau. This thesis discusses how together we can make this happen.

year: 2020