Context Based Visual Content Verification

TL;DR

This paper enhances visual content verification by integrating contextual information like location and environment, using a new annotated dataset, leading to a 16% accuracy improvement over traditional co-occurrence methods.

Contribution

Introduces a context-aware verification method and a new annotated dataset, AAVOC, to improve accuracy in visual content verification tasks.

Findings

Context integration improves verification accuracy by up to 16%.

New annotated dataset AAVOC supports training and evaluation.

Multi-level co-occurrence analysis benefits from contextual data.

Abstract

In this paper the intermediary visual content verification method based on multi-level co-occurrences is studied. The co-occurrence statistics are in general used to determine relational properties between objects based on information collected from data. As such these measures are heavily subject to relative number of occurrences and give only limited amount of accuracy when predicting objects in real world. In order to improve the accuracy of this method in the verification task, we include the context information such as location, type of environment etc. In order to train our model we provide new annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional properties of the image. We show that the usage of context greatly improve the accuracy of verification with up to 16% improvement.