Idanwo ti o sopọ mọ itọsọna · Rọrun Ipele

Idanwo Aworan ifori

Review image-captioning pipelines, attention, COCO annotations and evaluation measures, while recognizing object-hallucination risks.

Jẹmọ itọsọna awọn ọnaIfiweranṣẹ Aworan
Ibeere 1 ti 8

Kini eto ifori aworan kan gbejade bi o ti njade?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini eto ifori aworan kan gbejade bi o ti njade?
    • Apejuwe ede adayeba ti aworan naa
    • Ẹya ti o ga julọ ti aworan naa
    • Akojọ awọn ipoidojuko piksẹli nikan
    • Faili fisinuirindigbindigbin ti aworan naa
  2. Ninu opo gigun ti akole ti Ayebaye, ipa wo ni kooduopo wiwo ṣe?
    • O kọ ọrọ gbolohun ikẹhin nipasẹ ọrọ
    • O yọkuro awọn aṣoju ẹya lati aworan naa
    • O tumọ awọn akọle laarin awọn ede
    • O ṣe ikun girama ti akọle
  3. Kini idi ti akiyesi wulo ni ifori aworan?
    • O rọ aworan naa lati ṣafipamọ ibi ipamọ
    • O yọ iwulo fun eyikeyi data ikẹkọ kuro
    • O jẹ ki awoṣe idojukọ lori awọn agbegbe aworan ti o yẹ fun ọrọ kọọkan
    • O ṣe iyipada akọle sinu ohun
  4. Iru data wo ni o lo pupọ fun ikẹkọ ati igbelewọn ifori aworan?
    • MNIST
    • WikiText
    • LibriSpeech
    • MS COCO
  5. Kini o tumọ si fun oluyipada akọle lati ṣe ipilẹṣẹ awọn ọrọ 'autoregressively'?
    • O ṣe asọtẹlẹ ọrọ kọọkan nipa lilo aworan ati awọn ọrọ ti o ti ipilẹṣẹ titi di isisiyi
    • O ṣe agbejade gbogbo awọn ọrọ nigbakanna
    • O ṣe agbejade ọrọ kan ṣoṣo fun aworan kan
    • O yi aworan pada si awọn piksẹli
  6. Ipo ikuna ti o wọpọ ti awọn awoṣe ifori ti a mọ si 'hallucination' tọka si kini?
    • Ṣiṣejade awọn akọle ti o kuru ju
    • Apejuwe awọn nkan ti kii ṣe ni aworan gangan
    • Titumọ awọn akọle ti ko tọ
    • Nṣiṣẹ ju laiyara lori awọn aworan nla
  7. How many human captions were collected for each image in the original COCO Captions dataset?
    • One caption total for the entire dataset
    • No text descriptions were collected
    • Five captions per image
    • One hundred captions for every image
  8. What does CIDEr compare when evaluating an image caption?
    • Only the image’s pixel resolution
    • The caption’s audio waveform against a transcript
    • Whether the caption contains a certain number of words
    • The candidate caption against human reference captions using consensus-weighted n-grams