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9 results for “video stimuli”
Kara_Nonopai_video_stimuli
<p>Speakers of Kara Nonopai comment some video stimuli (a broken stick, a mango being put into a container, a fence being hit with a stick). </p> <p>The speakers are: </p> <p>Mrs. Delvin Apelis</p> <p>Mrs. Lilian Maturau</p> <p>Mr. Stanley Maturau</p> <p>Mrs. Dinah Killion </p> <p>Location: Nonopai village</p> <p>Date: October 17, 2022</p>
GENEA Challenge 2023 user-study video stimuli
<p>This Zenodo repository contains video stimuli in mp4 format from the user studies in the GENEA Challenge 2023.</p> <p> </p> <p><strong>Contents:</strong></p> <p>The file "monadic_videos.zip" contains the video stimuli used in the monadic evaluations of the challenge (human-likeness and speech appropriateness) and the file "dyadic_videos.zip" the video stimuli from the dyadic evaluation (appropriateness for the interlocutor).</p> <p>Video stimuli with mismatched motion are in the corresponding subfolders with the prefix "mismat_".</p> <p>The file "attention_check_examples.zip" contains examples of the attention-check videos used in the challenge.</p> <p> </p> <p>The release contains all videos used for the challenge evaluation, except for the attention-checks, where only a few examples are provided. Except for the attention-check examples for the human-likeness studies, all videos contain speech audio. This audio needs to be removed to replicate the human-likeness evaluation.</p> <p> </p> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-08-01) this is our ACM ICMI 2023 paper:</p> <p>Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.</p> <p>Also, please cite the paper about the original dataset from Meta Research:</p> <p>Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.</p> <p> </p> <p>Condition NA in the data contains motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These stimuli, and the audio, are licensed under a CC BY NC 4.0 international license. The motion for all other conditions is released under a CC BY 4.0 international license, whose license text is reproduced in the file LICENSE.txt.</p> <p> </p> <p><strong>More information:</strong></p> <p>To find more GENEA Challenge 2023 material on the web, please see:</p> <ul> <li> <p><a href="https://genea-workshop.github.io/2023/challenge/">https://genea-workshop.github.io/2023/challenge/</a></p> </li> </ul> <p>If you have any questions or comments, please contact:</p> <ul> <li> <p>The GENEA Challenge organisers <genea-challenge@googlegroups.com></p> </li> </ul>
Video data of spontaneous responses of common marmosets (Callithrix jacchus) on 3D and 2D cricket stimuli.
<p>The degree to which nonhuman animals recognize 2D images as representing the corresponding real objects remains debated. The common marmoset monkey (<em>Callithrix jacchus</em>) is often cited as a species which spontaneously shows natural behaviors to 2D images, e.g. grabbing behaviors to insects and fear responses to snakes. In this study, ten marmosets from two different groups were tested with a live cricket, a 3D plastic model, a monochrome image and two video recordings of the cricket.<br> The monkeys showed the grabbing behavior to the real cricket and the 3D plastic model, but to none of the 2D images. Our experiment suggests that depth information is the most important factor eliciting predatory behavior from the marmosets. In behavioral experiments, monkeys' responses toward 2D images of real objects should be carefully interpreted.</p> <p>All session videos are uploaded here with a 'LOG.txt' file which has sessions & timestamps when coded behaviors occurs.</p>
GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.
<p><strong>Recording setup</strong><br> Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".</p> <p><strong>Description of the dataset</strong><br> The dataset consists of following three subsets.</p> <p><em>1. Dataset_visual_change</em><br> The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:</p> <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> <p><em>2. Dataset_stimuli</em><br> Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and<br> the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files:</p> <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> <p>The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files:</p> <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> <p><em>3. Dataset_evaluation</em><br> We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.</p> <p><strong>Changelog</strong><br> [1.0.2] Add counts of layer pixels per participant.<br> [1.0.1] Change to CC0 license.<br> [1.0.1] Add labels of third annotator "l3".<br> [1.0.0] Initial release.</p>
Using a new video rating tool to crowd-source analysis of behavioural reaction to stimuli
Open the record for dataset details and reuse information.
GENEA Challenge 2022 user-study video stimuli
<p>This Zenodo repository contains user-study video stimuli in mp4 format for all test-set motion submitted by teams participating in the GENEA Challenge 2022.</p> <p> </p> <p>Contents:</p> <p>The "full-body_videos" zip file corresponds to the full-body tier of the challenge and the "upper-body_videos" file to the upper-body tier.</p> <p>Video stimuli with mismatched motion are in the corresponding folders with the prefix "mismatched_".</p> <p>The "attention_check_examples" zip file contains examples of the attention-check videos used in the challenge.</p> <p> </p> <p>The release contains all videos used for the challenge evaluation, except for the attention-checks, where only a few examples are provided. Except the attention-check examples for the human-likeness studies, all videos contain speech audio. This audio needs to be removed to replicate the human-likeness evaluation.</p> <p> </p> <p>Attribution:</p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-16) this is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p><a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p> </p> <p>Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the license text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>*<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/"> https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>*<a href="https://genea-workshop.github.io/2022/challenge/"> https://genea-workshop.github.io/2022/challenge/</a></p> <p> </p> <p>If you have any questions or comments, please contact:</p> <p>* The GENEA Challenge & Workshop organisers <genea-contact@googlegroups.com></p>
Video Stimuli
<p>Video stimulus materials used in each experimental task:<br> HiFrust_sync.avi - High frustration synchronization<br> HiFrust_NonSync.avi - High frustration non-synchronization<br> LoFrust_sync.avi - Low frustration synchronization<br> LoFrust_NonSync.avi - Low frustration non-synchronization</p>
GENEA Challenge 2020 user-study video stimuli
<p>This Zenodo repository contains all video stimuli used in the two crowdsourced user studies (appropriateness and human-likeness) of the GENEA Challenge 2020.</p>
Stimuli for "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video"
<p>This dataset contains all stimuli used in "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video" by Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh, Dong Young Kim, Andrew Lippman, and Rosalind Picard.</p> <p>The videos are contained in the Materials folder and the Data Folder provides two .csv files that map the video filenames with their metadata. </p> <p>For video sources, see Table 19 in the arXiv version of the paper: https://arxiv.org/abs/2202.12883</p>
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