Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the cha...

Recent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a single language, requiring multiple language-specific models to build a multilingual captioning system. We propose a very simple technique to build a single unified model across languages, using artificial tokens to control the lang...

We examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-theart neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result i...

The task of generating natural language descriptions from images has received a lot of attention in recent years. Consequently, it is becoming increasingly important to evaluate such image captioning approaches in an automatic manner. In this paper, we provide an in-depth evaluation of the existing image captioning metrics through a series of carefully designed experiments. Moreover, we explore...

As mentioned in our paper, we study a new problem, aesthetic critiques generation, which is different from conventional image captioning. Many recent works [1, 2] started to argued that conventional evaluation criteria (BLEU, METEOR and CIDEr) borrowed from machine translation community are unsuitable for image captioning task. How to choose a suitable criterion is still a tricky problem in ima...

We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sent...

The aim of image captioning is to generate similar captions by machine as human do to describe image contents. Despite many efforts, generating discriminative captions for images remains non-trivial. Most traditional approaches imitate the language structure patterns, thus tend to fall into a stereotype of replicating frequent phrases or sentences and neglect unique aspects of each image. In th...

With recent innovations in dense image captioning, it is now possible to describe every object of the scene with a caption while objects are determined by bounding boxes. However, interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes. Furthermore, in current captioning frameworks, the user is not able to involve personal preferences to exclude o...

Significant performance gains in deep learning coupled with the exponential growth of image and video data on the Internet have resulted in the recent emergence of automated image captioning systems. Ensuring scalability of automated image captioning systems with respect to the ever increasing volume of image and video data is a significant challenge. This paper provides a valuable insight in t...

With the huge expansion of Internet and trillions gigabytes data generated every single day, needs for development various tools have become mandatory in order to maintain system adaptability rapid changes. One these is known as image captioning. Every entity must be properly identified managed, therefore case data, automatic captioning identification required. Similarly, content generation mis...