ronghanghu

Call For Papers — ACL 2020 Workshop on Advances in Language and Vision Research (ALVR)

Workshop Website: http://alvr-workshop.github.io

Overview

Language and vision research has attracted great attention from both natural language processing (NLP) and computer vision (CV) researchers. Gradually, this area is shifting from passive perception, templated language, and synthetic imagery/environments to active perception, natural language, and photo-realistic simulation or real world deployment. Thus far, few workshops on language and vision research have been organized by groups from the NLP community. We propose the first workshop on Advances in Language and Vision Research (ALVR) in order to promote the frontier of language and vision research and to bring interested researchers together to discuss how to best tackle and solve real-world problems in this area.

This workshop covers (but is not limited to) the following topics:

• New tasks and datasets that provide real-world solutions in the intersection o

ronghanghu

import json
import re


class EvalAIAnswerProcessor:
    """
    Processes an answer similar to Eval AI
        copied from
        https://github.com/facebookresearch/pythia/blob/c46b3b3391275b4181567db80943473a89ab98ab/pythia/tasks/processors.py#L897
    """

ronghanghu

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

ronghanghu

import matplotlib.pyplot as plt
import os.path as osp
import numpy as np

angle_inc = np.pi / 6.


def print_bbox(bbox):
    x1, y1, x2, y2 = bbox
    plt.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], 'r-')

ronghanghu

ronghanghu

import tensorflow as tf
from glob import glob
import numpy as np


sess = tf.Session(config=tf.ConfigProto(
    gpu_options=tf.GPUOptions(allow_growth=True)))


N = 100  # batch_size

ronghanghu

ronghanghu

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np
import logging
import os
import cv2
from laser.client.py import LaserClient, Options

ronghanghu

#!/usr/bin/env python2

import json
import sys

with open(sys.argv[1]) as data_file, \
     open("ids.txt", "w") as id_file, \
     open("questions.txt", "w") as question_file:
  data = json.load(data_file)
  questions = data["questions"]

ronghanghu

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x7fe3fc03dd10>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: Tesla K40c
major: 3 minor: 5 memoryClockRate (GHz) 0.745
pciBusID 0000:88:00.0