SPIDER-MAN: HOMECOMING - Virtual Reality Experience

SPIDER-MAN: HOMECOMING - Virtual Reality Experience

Experience VR through the eyes of Spider-Man. Try it in select @Cinemark Theaters across the country and see #SpiderManHomecoming in theater

Virtual Reality, Game Development

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Sony Pictures Virtual Reality announced a new VR experience for Columbia Pictures’  upcoming “Spider-Man: Homecoming” flick Friday that will let players experience how it feels like to be Spidey. They’ll be able to do some target practice with Spider-Man’s new web shooters, and sling themselves through the air to face off against Spider-Man’s arch-nemesis, The Vulture. The experience will be available for free across all major VR platforms, including PlayStation VR, Oculus Rift and HTC Vive on June 30, a week before the movie is scheduled to hit theaters.




Variety / Hollywood Article

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Avideep M. updated status

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Avideep Mukherjee

I am currently working on kNN classifiers. I want to know a simple case where weighted kNN classifier outperforms kNN classifier. Any help would be appreciated.

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AMIRSINA T. created project Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks

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Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks

Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multispeaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this paper. We propose the use of a coupled 3D convolutional neural network (3D CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller dataset for training, our proposed method surpasses the performance of the existing similar methods for audio-visual matching, which use 3D CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 20% on the equal error rate and over 7% on the average precision in comparison to the state-of-the-art method.

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Yuanzheng C. created project ColorEvangelist

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Sketch/Line Art colorization is a time consuming process, The automation of the process requires not just simple boundary detection but also semantical feature identification, user interaction and shading(which is not a problem for grey image colorization). We propose an deep end-to-end trainable colorization model that meanwhile small in size and has a almost-real-time performance. It identifies semantic features from the sketch and inpaint it with/without user interaction with realistic shading.

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Alexander L. created project Analyzing Radio Telescope Array Big Data Using Intel DevCloud TensorFlow

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Analyzing Radio Telescope Array Big Data Using Intel DevCloud TensorFlow

For decades, institutional data on radio telescope arrays have accumulated in American astronomical research institutions.

Radio array telescopes like SETI at Home and the NRAO Very Large Array have over time, amassed big, not just large, big data sets of astronomical data concerning the cosmos. Data points such as polarization, radio wave frequency, magnitude, longitude/latitude, and date/time help astronomers pinpoint and understand stars.

We seek to use TensorFlow integrated into Intel DevCloud to build a TensorFlow Machine Learning model which better interprets and analyzes radio telescope array big data and also to utilize machine vision to further enhance radio images created by astronomical big data.

Mentored by Professor Micheal Strong at Cosumnes River College, Sacramento CA. 2018

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