Oracle performance for visual captioning

11/14/2015
by   Li Yao, et al.
0

The task of associating images and videos with a natural language description has attracted a great amount of attention recently. Rapid progress has been made in terms of both developing novel algorithms and releasing new datasets. Indeed, the state-of-the-art results on some of the standard datasets have been pushed into the regime where it has become more and more difficult to make significant improvements. Instead of proposing new models, this work investigates the possibility of empirically establishing performance upper bounds on various visual captioning datasets without extra data labelling effort or human evaluation. In particular, it is assumed that visual captioning is decomposed into two steps: from visual inputs to visual concepts, and from visual concepts to natural language descriptions. One would be able to obtain an upper bound when assuming the first step is perfect and only requiring training a conditional language model for the second step. We demonstrate the construction of such bounds on MS-COCO, YouTube2Text and LSMDC (a combination of M-VAD and MPII-MD). Surprisingly, despite of the imperfect process we used for visual concept extraction in the first step and the simplicity of the language model for the second step, we show that current state-of-the-art models fall short when being compared with the learned upper bounds. Furthermore, with such a bound, we quantify several important factors concerning image and video captioning: the number of visual concepts captured by different models, the trade-off between the amount of visual elements captured and their accuracy, and the intrinsic difficulty and blessing of different datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/28/2020

Fusion Models for Improved Visual Captioning

Visual captioning aims to generate textual descriptions given images. Tr...
research
04/25/2015

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

In this paper, we address the task of learning novel visual concepts, an...
research
06/06/2019

Context-Aware Visual Policy Network for Fine-Grained Image Captioning

With the maturity of visual detection techniques, we are more ambitious ...
research
06/02/2021

Learning to Select: A Fully Attentive Approach for Novel Object Captioning

Image captioning models have lately shown impressive results when applie...
research
11/21/2016

Dense Captioning with Joint Inference and Visual Context

Dense captioning is a newly emerging computer vision topic for understan...
research
01/23/2022

An Integrated Approach for Video Captioning and Applications

Physical computing infrastructure, data gathering, and algorithms have r...
research
11/24/2021

Universal Captioner: Inducing Content-Style Separation in Vision-and-Language Model Training

While captioning models have obtained compelling results in describing n...

Please sign up or login with your details

Forgot password? Click here to reset