Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset.
Code (2)
Tasks
Image CaptioningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The MSR-Video to Text Dataset with Clean Annotations
Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR…
SentenceVideo CaptioningDoes the Performance of Text-to-Image Retrieval Models Generalize Beyond Captions-as-a-Query?
Text-image retrieval (T2I) refers to the task of recovering all images relevant to a keyword query. Popular datasets for text-image retrieval, such as Flickr30k, VG, or MS-COCO, utilize annotated image captions, e.g., “a…
DescriptiveImage CaptioningImage RetrievalRetrievalConceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected w…
Caption GenerationDiversityImage CaptioningQuestion Answering+2Are Large Language Models Good Data Preprocessors?
High-quality textual training data is essential for the success of multimodal data processing tasks, yet outputs from image captioning models like BLIP and GIT often contain errors and anomalies that are difficult to rec…
Image CaptioningContext-Aware Group Captioning via Self-Attention and Contrastive Features
While image captioning has progressed rapidly, existing works focus mainly on describing single images. In this paper, we introduce a new task, context-aware group captioning, which aims to describe a group of target ima…
Image Captioning