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8 results for “actional classification”
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
An appendix to the article: "'Completive' semantic component and a lexeme-based approach to actional classification in Russian" (Приложение к статье: «Семантический компонент 'комплетив' и «полексемный» подход к акциональной классификации в русском языке»)
<p>An appendix to the article “‘Completive’ semantic component and a lexeme-based approach to actional classification in Russian” by Maksim (L.) Fedotov [in Russian] (<em>Voprosy Jazykoznanija</em>, 2019, 3: 7–44. URL: <a href="http://vja.ruslang.ru/en/archive/2019-3/7-44">http://vja.ruslang.ru/en/archive/2019-3/7-44</a>. DOI: <a href="https://doi.org/10.31857/S0373658X0004896-4">10.31857/S0373658X0004896-4</a>). The appendix contains a table, in which 45 Russian perfective and imperfective verbs are analyzed by applying to them a system of tests descibed in the article and thus assigning to them an actional (aspectual) characteristic.</p> <p>Приложение к статье М. Л. Федотова «Семантический компонент ‘комплетив’ и «полексемный» подход к акциональной классификации в русском языке» (<em>Вопросы языкознания</em>, 2019, 3: 7–44. URL: <a href="http://vja.ruslang.ru/ru/archive/2019-3/7-44">http://vja.ruslang.ru/ru/archive/2019-3/7-44</a>. DOI: <a href="https://doi.org/10.31857/S0373658X0004896-4">10.31857/S0373658X0004896-4</a>). Приложение содержит таблицу с анализом 45 глаголов русского языка совершенного и несовершенного вида, к которым применяется описанная в статье система тестов, и которым на её основании присваиваются те или иные акциональные ярлыки.</p> <p>* * *</p> <p>Annotations for the article itself:</p> <p>Maksim (L.) Fedotov. <em>‘‘Completive’ semantic component and a lexeme-based approach to actional classification in Russian</em> [in Russian].</p> <p>The paper discusses two related aspectological topics. First section examines the ‘completive’ — i. e. ‘attainment of the internal limit’ — meaning (together with its counterpart ‘incompletive’, i. e. ‘non-attainment of the internal limit’). Its localization in the semantic structure of the utterance is determined: between aspect proper and actionality proper. Also, ‘completive’ can be included under the semantic scope of an iterative operator. It is argued that ‘completive’ is contained as a fixed component in the semantics of some Russian Imperfective verbs such as <em>sgorat’ </em>‘burn (down)’ and <em>pročityvat’ </em>‘read (through)’.</p> <p>Second section demonstrates practical possibility and the advantages of a single-verb approach to actional classification in Russian, an approach which is not based on the notion of aspectual pairs. Actional properties are ascribed separately to single Perfective and Imperfective verbs on the basis of uniform tests. The efficiency of the approach is demonstrated on a pilot sample of Perfective and Imperfective verbs.</p> <p>М. Л. Федотов. <em>Семантический компонент ‘комплетив’ и «полексемный» подход к акциональной классификации в русском языке</em>.</p> <p>В статье обсуждаются два связанных аспектологических сюжета. Во-первых, рассматривается значение ‘комплетив’, т. е. ‘достижение предела’ (а также парное ‘инкомплетив’, т. е. ‘недостижение предела’). Определяется его локализация в семантической структуре высказывания: между собственно аспектом и собственно акциональностью. Также ‘комплетив’ может включаться в сферу действия итеративного оператора. Аргументируется гипотеза о наличии фиксированного компонента ‘комплетив’ в семантике русских глаголов НСВ типа <em>сгорать </em>и <em>прочитывать</em>.</p> <p>Во-вторых, демонстрируется практическая возможность и преимущества «полексемного» — без апелляции к видовым парам — подхода к акциональной классификации в русском языке. Акциональные характеристики приписываются отдельным глаголам совершенного вида (СВ) и несовершенного вида (НСВ) на основании однотипных тестов. Работоспособность метода демонстрируется на тестовой выборке глаголов СВ и НСВ.</p>
Fig. 1 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 1 Scheme of the phylogenetic relationships of the main groups of land planarians after Álvarez-Presas et al. (2008) using Sluys et al. (2009) nomenclature.
Fig. 3 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 3 Phylogenetic tree inferred from the concatenated data set by maximum likelihood (ML). Squares at nodes indicate the presence and/ or level of support of the node under the different parameter schemes as sketched in the legend; black indicates maximum support. Symbols over the nodes denote ≥ 95% bootstrap support obtained in the ML (bluish triangles) and maximum posterior probability values (1) in Bayesian inference analyses (orangish circles). Numbers following species names refer to the accession numbers of the specimens deposited in the section Platyhelminthes of the MZUSP (MZUSP PL). The notation p indicates the species pictured on the right side (pictures not to scale). Scale bar is given in number of substitutions per site.
Fig. 1 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 1 Scheme of the phylogenetic relationships of the main groups of land planarians after Álvarez-Presas et al. (2008) using Sluys et al. (2009) nomenclature.
Training data for Xenopus nucleus for Oneat action classification
<p>Contains the npz files to train 4D oneat network for classification of xenopus nuclei into Normal and Mitosis cell types. In conjunction with the MTVKW repository this data contains patches of TZYX (3,8,64,64) that is then used to train the oneat network. Statistics</p> <p> </p> <table summary="The amount of training data generated for Xenopus action classification "> <caption>Statistics</caption> <tbody> <tr> <td>Normal</td> <td>Mitosis</td> <td>Normal(Aug)</td> <td>Mitosis(Aug)</td> </tr> <tr> <td>12530</td> <td>2796</td> <td>4178+4176</td> <td>932+932</td> </tr> </tbody> </table> <p> </p> <p>Aug = Augmentation using Poisson and Gaussian noise and flip XY transformation.</p> <p>The training data image size = (7663,3,8,64,64,1)</p> <p>The training data label size = (7663,3,10,1)</p> <p>Script: https://github.com/kapoorlab/MTVKW/blob/main/minus_06_create_oneat_patches.py</p>
Training data for Spheroid nucleus for Oneat action classification
<p>Contains the npz files to train 4D oneat network for classification of spheroid nuclei into Normal, Mitosis and Apoptosis cell types. In conjunction with the BLVKW repository this data contains patches of TZYX (3,8,64,64) that is then used to train the oneat network. Statistics</p> <p> </p> <table summary="The amount of training data generated for Xenopus action classification "> <caption>Statistics</caption> <tbody> <tr> <td>Normal</td> <td>Mitosis</td> <td>Apoptosis</td> </tr> <tr> <td> <p>7672</p> </td> <td> <p>1680</p> </td> <td>357</td> </tr> </tbody> </table> <p> </p> <p>Aug = Augmentation using Poisson and Gaussian noise and flip XY transformation.</p> <p>The training data image size = (9709,3,8,64,64,1)</p> <p>The training data label size = (9709,3,11,1)</p> <p>Script: https://github.com/kapoorlab/BLVKW/blob/main/minus_06_create_oneat_patches.py</p>
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