文献紹介:PolyViT: Co-training Vision Transformers on Images, Videos and AudioToru Tamaki
Valerii Likhosherstov, Anurag Arnab, Krzysztof Choromanski, Mario Lucic, Yi Tay, Adrian Weller, Mostafa Dehghani, "PolyViT: Co-training Vision Transformers on Images, Videos and Audio" arXiv2021
https://arxiv.org/abs/2111.12993
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
A Style-Based Generator Architecture for Generative Adversarial Networks (https://arxiv.org/abs/1812.04948)
の解説スライドです。
文献紹介:PolyViT: Co-training Vision Transformers on Images, Videos and AudioToru Tamaki
Valerii Likhosherstov, Anurag Arnab, Krzysztof Choromanski, Mario Lucic, Yi Tay, Adrian Weller, Mostafa Dehghani, "PolyViT: Co-training Vision Transformers on Images, Videos and Audio" arXiv2021
https://arxiv.org/abs/2111.12993
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
A Style-Based Generator Architecture for Generative Adversarial Networks (https://arxiv.org/abs/1812.04948)
の解説スライドです。
第1回 Japan Xamarin User Group Conference の発表資料です。
資料とサンプルのソースは https://github.com/amay077/jxug_1_xamarin_forms_talk
The document discusses various topics related to Android development including:
1. Lists several popular running and exercise tracking apps for Android.
2. Discusses techniques for customizing the Android action bar and removing the title bar from an activity's view.
3. Provides examples of creating gradient colors for use in activities and the action bar.
This document discusses fragments in Android. It covers defining fragments in XML layout files using the <fragment> tag, communicating between fragments and activities, and how fragments were implemented before Android 3.0 using ActivityGroup and LocalActivityManager which are now deprecated. The document also discusses how fragments are used to modularize parts of an app's UI and how they are meant to be reusable components.
The document discusses an Android layout cookbook seminar that covers improving functions, the layout hierarchy, tabs versus buttons, menus, action bars, asynchronous tasks, and progress. The seminar was created by @vvakame and includes topics like the main function, sub functions, QR codes, long taps, and a coffee break.
The document discusses Android layout concepts including margin, padding, LinearLayout, ImageView, GridView, and ListView. It explains that padding is the space between the content of a view and its border, while margin is the space outside the border of a view. It also distinguishes between base/main color and point color.
61. @Override
public Parcelable onSaveInstanceState() {
Parcelable superState = super.onSaveInstanceState();
SavedState ss = new SavedState(superState);
ss.imageUrl = imageUrl;
if (localImageUri != null) {
ss.uriString = localImageUri.toString();
}
return ss;
}
@Override
public void onRestoreInstanceState(Parcelable state) {
SavedState ss = (SavedState) state;
super.onRestoreInstanceState(ss.getSuperState());
if (!TextUtils.isEmpty(ss.uriString)) {
setImageFromUriString(ss.uriString);
} else if (!TextUtils.isEmpty(ss.imageUrl)) {
setImageUrl(ss.imageUrl);
}
requestLayout();
}
63. final int id = eventGroup.getCheckedRadioButtonId();
switch (id) {
case R.id.attend:
profileData.eventStatus = ProfileData.EVENT_STATUS_ATTEN
break;
case R.id.not_attend:
profileData.eventStatus = ProfileData.EVENT_STATUS_NOT_A
break;
case R.id.unknown:
profileData.eventStatus = ProfileData.EVENT_STATUS_UNKNO
break;
default:
return;
}