Brief Review — Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Conceptual Captions, Over 3M <Image, Caption> Pairs
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning,
Conceptual Captions, by Google AI,
2018 ACL, Over 700 Citations (Sik-Ho Tsang @ Medium)
Vision Language Model (VLM), Image Captioning, Transformer
- Conceptual Captions, an image captioning dataset, is proposed, which has an order of magnitude more images than the MS-COCO dataset.
- An image captioning model is proposed as baseline, where Inception-ResNet-v2, as in Inception-v4, is used for image-feature extraction and Transformer for sequence modeling.
- Conceptual Captions: Dataset Generation Process
- Image Captioning Model
1. Conceptual Captions: Dataset Generation Process
- This pipeline processes billions of Internet webpages in parallel. From these webpages, it extracts, filters, and processes candidate <image, caption> pairs.
1.1. Image-based Filtering
- It only keeps JPEG images where both dimensions are greater than 400 pixels. Ratio cannot be smaller or larger than 2. Images that trigger pornography or profanity detectors are excluded.
- These filters discard more than 65% of the candidates.
1.2. Text-based Filtering
- It harvests Alt-text from HTML webpages.
- Google Cloud Natural Language APIs are used to analyze candidate Alt-text. Heuristics are also introduced.
- These filters only allow around 3% of the incoming candidates to pass to the later stages.
1.3. Image & Text-based Filtering
- Google Cloud Vision APIs are used to assign class labels to images. Images are generally assigned between 5 to 20 labels. These labels are matched against the candidate text.
- Candidates for which none of the text tokens can be mapped to the content of the image, are filtered out.
1.4. Text Transformation with Hypernymization
- Some rules are shown above.
- e.g.: named-entities are identified, matched against the KG entries, and substitute with their hypernym, using the Google Knowledge Graph (KG) Search API.
- Around 20% of samples are discarded during this transformation because it can leave sentences too short or inconsistent.
- These remaining <image, caption> pairs contain around 16,000 entity types.
1.5. Conceptual Captions Quality
- A random sample of 4K examples are extracted from the test split.
- Out of 3 annotations, over 90% of the captions receive a majority (2+) of GOOD judgments. This indicates that the above pipeline produces high-quality image captions.
The training set consists of slightly over 3.3M examples, while there are slightly over 28K examples in the validation set and 22.5K examples in the test set.
- The size of the training set vocabulary (unique tokens) is 51,201.
2. Image Captioning Model
- A deep CNN that takes a (preprocessed) image and outputs a vector of image embeddings X.
- An Encoder module that takes the image embeddings and encodes them into a tensor H.
- A Decoder model that generates outputs zt at each step t, conditioned on H as well as the decoder inputs Y1:t.
- Inception-ResNet-v2, as in Inception-v4, as the CNN component.
- Two sequence models are tried: One is modified Show and Tell (RNN-based model). One is based on Transformer (T2T).
The goal is to produce baseline results.
3.1. Qualitative Results
- e.g.: For the left-most image, COCO-trained models use “group of men” to refer to the people in the image; Conceptual-based models use the more appropriate and informative term “graduates”.
3.2. Human Evaluation
Conceptual-based models are superior. In 50.6% (for the T2T8x8 model) of cases, a majority of annotators (2+) assigned a GOOD label.
3.3. Auto Metric
- For all metrics, higher number means closer distance between the candidates and the ground-truth captions.
- Different test sets are tried.
The automatic metrics fail to corroborate the human evaluation results.
[2018 ACL] [Conceptual Captions]
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning