Perceiving animacy from shape

Superordinate visual classification—for example, identifying an image as “animal,” “plant,” or “mineral”—is computationally challenging because radically different items (e.g., “octopus,” “dog”) must be grouped into a common class (“animal”). It is plausible that learning superordinate categories teaches us not only the membership of particular (familiar) items, but also general features that are shared across class members, aiding us in classifying novel (unfamiliar) items. Here, we investigated visual shape features associated with animate and inanimate classes. One group of participants viewed images of 75 unfamiliar and atypical items and provided separate ratings of how much each image looked like an animal, plant, and mineral. Results show systematic tradeoffs between the ratings, indicating a class-like organization of items. A second group rated each image in terms of 22 midlevel shape features (e.g., “symmetrical,” “curved”). The results confirm that superordinate classes are associated with particular shape features (e.g., “animals” generally have high “symmetry” ratings). Moreover, linear discriminant analysis based on the 22-D feature vectors predicts the perceived classes approximately as well as the ground truth classification. This suggests that a generic set of midlevel visual shape features forms the basis for superordinate classification of novel objects along the animacy continuum

Pose Induction for Novel Object Categories

We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes. We present a generalized classifier that can reliably induce pose given a single instance of a novel category. In case of availability of a large collection of novel instances, our approach then jointly reasons over all instances to improve the initial estimates. We empirically validate the various components of our algorithm and quantitatively show that our method produces reliable pose estimates. We also show qualitative results on a diverse set of classes and further demonstrate the applicability of our system for learning shape models of novel object classes

Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural network is trained on a subset of objects classified by the human eye and we test whether the machine learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile-fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artifacts. Using a set of twelve parameters, the neural network is able to reproduce the human classifications to better than 90% for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine- learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes.Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS. Revised to match accepted version

PENGEMBANGAN MEDIA PEMBELAJARAN KOTARANG (KOPER TAMBAH KURANG) PADA MATA PELAJARAN MATEMATIKA DI KELAS II SEKOLAH DASAR

Learning takes place at SD Muhammadiyah 4 Batu which uses the Merdeka curriculum in classes I, II, IV and V, while classes III and VI still use curriculum 13. Learning will be effective and enjoyable if accompanied by learning media. . The aim of this research is to develop Kotarang learning media that motivates students to learn. With the development of learning media, it is hoped that students will be able to understand addition and subtraction material in class II elementary school mathematics learning. This study uses the ADDIE development model consisting of 5 stages, namely (1) analyze, (2) design, (3) development, (4) implementation, and (5) evaluation. ). Data collection techniques use observation, interviews and documentation. The data analysis techniques used are qualitative data analysis techniques and quantitative data analysis techniques. Data were taken in class II at Muhammadiyah 4 Batu Elementary School with a total of 25 students and carried out on September 6-7 2023. The results of this study resulted in a product in the form of Kotarang learning media. The research has gone through the stages of validation, namely material validation to get a percentage of 93% and media experts 92%. This study received good responses from students as evidenced by the evaluation results of 25 children getting an average score of 86,8%. Suggestions for further research are to develop learning media better, in terms of appearance, shape and how to use it, so that it is even more practical. The guidebook can be designed in more detail. The marble media can be replaced with other media to make its use more practical

Detection of major ASL sign types in continuous signing for ASL recognition

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker

Between-class Learning for Image Classification

In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. BC learning has the ability to impose constraints on the shape of the feature distributions, and thus the generalization ability is improved. BC learning is originally a method developed for sounds, which can be digitally mixed. Mixing two image data does not appear to make sense; however, we argue that because convolutional neural networks have an aspect of treating input data as waveforms, what works on sounds must also work on images. First, we propose a simple mixing method using internal divisions, which surprisingly proves to significantly improve performance. Second, we propose a mixing method that treats the images as waveforms, which leads to a further improvement in performance. As a result, we achieved 19.4% and 2.26% top-1 errors on ImageNet-1K and CIFAR-10, respectively.Comment: 11 pages, 8 figures, published as a conference paper at CVPR 201

How Teachers’ Roles Shape Adult Literacy Learners’ Engagement in Instruction

This paper addresses the question: What are the contextual factors that shape learners’ engagement in adult literacy education? Six adult literacy classes were studied at an urban adult learning center. Data sources included video, traditional ethnographic observation, stimulated recall interviews and open interviews. Findings focus on the ways that teachers’ roles shape engagement