Automatically captioning images with natural language sentences is an important research topic. State of the art models are able to produce human-like sentences. These models typically describe the depicted scene as a whole and do not target specific objects of interest or emotional relationships between these objects in the image. However, marketing companies require to describe these importan...

The ability to recognize image features and generate accurate, syntactically reasonable text descriptions is important for many tasks in computer vision. Auto-captioning could, for example, be used to provide descriptions of website content, or to generate frame-by-frame descriptions of video for the vision-impaired. In this project, a multimodal architecture for generating image captions is ex...

Recently, deep neural network models have achieved promising results in image captioning task. Yet, “vanilla” sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagements, contexts, and user intentions. To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate ...

We address the problem of video captioning by grounding language generation on object interactions in the video. Existing work mostly focuses on overall scene understanding with often limited or no emphasis on object interactions to address the problem of video understanding. In this paper, we propose SINet-Caption that learns to generate captions grounded over higher-order interactions between...

AbstractWith the advent of rich visual representations and pre-trained language models, video captioning has seen continuous improvement over time. Despite performance improvement, models are prone to hallucination. Hallucination refers generation highly pathological descriptions that detached from source material. In captioning, there two kinds hallucination: object action Instead endeavoring ...

We present a data-driven framework for image caption generation which incorporates visual and textual features with varying degrees of spatial structure. We propose the task of domain-specific image captioning, where many relevant visual details cannot be captured by off-the-shelf general-domain entity detectors. We extract previously-written descriptions from a database and adapt them to new q...

To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propos...

In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional neural network (CNN) feature, we propose to represent the input image as a sequen...