Decoupled Novel Object Captioner

Posted by JoselynZhao on October 24, 2019

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Abstract

In this paper, we introduce the zero-shot novel object caption-ing task where the machine generates descriptions without extratraining sentences about the novel object. To tackle the challenging problem, we propose a Decoupled Novel Object Captioner (DNOC)framework that can fully decouple the language sequence model from the object descriptions.

**DNOC has two components. ** 1) ASequence Model with the Placeholder (SM-P) generates a sen-tence containing placeholders.

占位符的ASequence模型(SM-P)生成一个包含占位符的传感器。

2) A key-value object memorybuilt upon the freely available detection model, contains the visualinformation and the corresponding word for each object.

Introduction

The captainingnetworks need a large number of image-sentence paired data totrain a meaningful model.

These captioning models fail in describing the novel objects whichare unseen words in the paired training data.

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However, to feed the novel object description into the generatedcaptions, existing approaches either employ the pre-trained lan-guage sequence model [3, 34] or require extra unpaired training sentences of the novel object [41].

**In both cases, the novel objectshave been used in training and, hence, is not really novel. **

A moreprecise meaning of novel in existing works is unseen in the pairedtraining sentences.

In this paper, we tackle the image captioning for novel objects,where we do not need any training sentences containing the object

We utilize a pre-trained object detection model about the novel object. We call it zero-shot novel object captioning to distinguish itfrom the traditional problem setting [3, 34, 41].

In the zero-shot novel object captioning, there are zero training sentences aboutthe novel object .i.e., there is no information about the semanticmeaning, sense, and context of the object

To address this problem, we propose a Decoupled Novel ObjectCaptioner (DNOC) framework that is able to generate natural lan-guage descriptions without extra training sentences of the novelobject.

in Fig. 1, our method first generates the captioning sentence bygenerating a placeholder “” to represent any novel object.Then it learns to fill in the placeholder with “zebra” based on thevisual object detection result.

the main contributions of this work are listed asfollows:

  • We introduce the zero-shot novel object captioning task
  • we design the sequence modelwith the placeholder (SM-P).
  • A key-value object memory is introduced to incorporate ex-ternal visual knowledge.

Methods

Preliminaries

given an input image I , the goal is to generate an as-sociated natural language sentence s of length nl, denoted as s = 在这里插入图片描述 在这里插入图片描述

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Zero-Shot Novel Object Captioning.

We denote Wunseen as the vocabulary for the novelobject words which are unseen in training.

A notable challenge for this task is to deal with the out-of-vocabulary (OOV) words.

The learned word embedding function ϕw is unable to encode the unseen words, since these word cannot simply be found in Wpaired.

We denote these extra training sentences as Sunpaired.

Sequence Model with the Placeholder

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To solve this problem, we design a newtoken, denoted as “<PL>”. “<PL>” is the placeholder that representsany novel words ˜w ∈ Wunseen . We add the token“” into the paired vocabulary Wpair edto learn the embedding.

our model utilizes theexternal knowledge from the object detection model to replace it

we use the LSTM as the backbone of our SM-P

Instead, the SM-P model outputs the “”token when it needs to generate a word.

The “” token will **be replaced by** the novel word generatedby the key-value object memory.

Key-Value Object Memory

we exploit a pre-trained object detection model tobuild the key-value object memory. 在这里插入图片描述

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Framework Overview

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** For an input image with novel objects, we have thefollowing steps to generate the captioning sentence**:

  • (i) We first exploit the SM-P to generate a captioning sentencewith some placeholders. Each placeholder represents an un-seen word/phrase for a novel object;
  • (ii) We then build a key-value object memory Mobjfor each inputbased on the detection feature-label pairs {fi , li } on the image;
  • (iii) Finally, we replace the placeholders of the sentence by corre-sponding object descriptions.

Training

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source code: https://github.com/Yu-Wu/Decoupled-Novel-Object-Captioner

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