codingPingjun

import pickle
import numpy as np
import pdb

img_width, img_height = 300, 300
box_configs = [
    {'layer_width': 38, 'layer_height': 38, 'num_prior': 3, 'min_size':  30.0,
     'max_size': None, 'aspect_ratios': [1.0, 2.0, 1/2.0]},
    {'layer_width': 19, 'layer_height': 19, 'num_prior': 6, 'min_size':  60.0,
     'max_size': 114.0, 'aspect_ratios': [1.0, 1.0, 2.0, 1/2.0, 3.0, 1/3.0]},

batFINGER

bl_info = {
    "name": "Mercator Project",
    "author": "batFINGER",
    "version": (1, 0),
    "blender": (2, 79, 0),
    "location": "View3D > Mesh > UV UnWrap > Mercator Project",
    "description": "UV Mercator Projection",
    "warning": "",
    "wiki_url": "",
    "category": "UV",

jin-zhe

Activity Recognition Datasets

An overview of recent action recognition datasets and their detection classes

Concepts & terminologies:

• Action: Atomic low-level movement such as standing up, sitting down, walking, talking etc.
• Activity/event: Higher level occurence then actions such as dining, playing, dancing
• Trimmed video: A short video clip containing event/action/activity of interest
• Untrimmed video: A video clip of arbitrary length potentially containing durations without activities of interest
• Localization: locating an instance of event/action/activity within a video at a spatial or temporal scale
• Spatial localization: Locating the region/area of an instance of action/activity within a video