Main

related bits

0

processing priority

4

site type

3 (personal blog or private political site, e.g. Blogspot, Substack, also small blogs on own domains)

review version

11

html import

20 (imported)

Events

first seen date

2024-11-05 15:42:28

expired found date

-

created at

2024-11-05 15:42:28

updated at

2026-03-02 01:15:50

Domain name statistics

length

32

crc

4389

tld

2211

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

13642151 (wordpress.com)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

164361

mp size raw text

4541

mp inner links count

9

mp inner links status

20 (imported)

Open Graph

title

Vision and Reasoning

description

Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering Somak Aditya, Yezhou Yang,  Chitta Baral ABSTRACT: Many vision and language tasks require commonsense reasoning be

site name

Vision and Reasoning

author

updated

2026-02-23 04:14:10

raw text

Vision and Reasoning Skip to content Vision and Reasoning ☰ Menu Home Contact Blog Facebook LinkedIn Twitter Instagram Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering Somak Aditya , Yezhou Yang ,   Chitta Baral ABSTRACT: Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Zastosowania AI (149)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

3471

text words

638

text unique words

317

text lines

68

text sentences

24

text paragraphs

9

text words per sentence

26

text matched phrases

0

text matched dictionaries

0

RSS

rss status

32 (unknown)

rss found date

2024-11-05 15:42:28

rss size orig

13686

rss items

1

rss spam phrases

0

rss detected language

1 (English)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap status

40 (completed successful import of reports.txt file to table in_pages)

sitemap review version

2

sitemap urls count

5

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

2024-11-05 15:42:38

sitemap process date

2024-11-16 22:24:21

sitemap first import date

-

sitemap last import date

2025-10-26 22:56:57