Image captioning, the ability to automatically generate written descriptions of images, is an important capability in artificial intelligence that has seen rapid advances in recent years. Models like Google‘s GIT (Generative Image Transformer), Facebook‘s BLIP (Bootstrapping Language-Image Pre-training), and OpenAI‘s ViT+GPT2 combine computer vision and natural language processing to "see" images and describe them in text.
In this in-depth guide, we will compare three leading image captioning models – GIT, BLIP, and ViT+GPT2 – on important criteria like accuracy, detail, and context to determine which performs the best for real-world usage.
Overview of Models
Before diving into the comparisons, let‘s briefly introduce each model:
GIT
Created by Google, GIT is built on a transformer architecture. It leverages both unlabeled image-text data from the internet as well as human-labeled data to generate descriptive and accurate captions. GIT comes in both a Base and Large version, with the latter having significantly more parameters.
BLIP
Developed by Facebook AI, BLIP takes a different approach by first pre-training a single neural network on image-text data before fine-tuning it for image captioning. This allows BLIP to develop a tight linkage between visual concepts and language.
ViT+GPT2
This model combines OpenAI‘s image recognition model ViT (Vision Transformer) with their language model GPT2. ViT encodes images into feature vectors which are passed to GPT2 to generate captions. The smaller model sizes of ViT and GPT2 make this a lightweight option.
Now that we know what we‘re working with, let‘s see how these models stack up!
Test Image 1: Man in Suit with Arms Crossed
Our first test image depicts a man in a suit standing with his arms crossed, with some additional details in the background. Let‘s see what captions the models produced:
GIT Base: A man wearing a suit with his arms crossed standing in front of a wall. There is a small rocket launcher on a podium behind him.
GIT Large: A man in a suit standing with his arms crossed.
BLIP: A man wearing a suit and tie standing with his arms crossed.
ViT+GPT2: A man in a suit standing with his arms crossed.
For this image, GIT Base provides the most descriptive caption, accurately depicting the man‘s pose and noting the odd detail of the rocket launcher in the background which a human may also find noticeable.
GIT Large and the other models miss this additional context. However, BLIP is the only model that correctly identifies the tie. ViT+GPT2 produces an accurate but very simple caption.
Winner: GIT Base
Test Image 2: Batman with Fire Background
Our next image is a bit surreal, with Batman holding his arms out in front of a background of fire. Let‘s examine the results:
GIT Base: A picture of Batman standing in front of a fiery background.
GIT Large: Batman standing against an orange background.
BLIP: A man dressed as Batman standing in front of an orange background.
ViT+GPT2: Batman standing with his arms open, with fire raging behind him.
For this image, ViT+GPT2 provides the most detailed and imaginative caption, accurately identifying Batman and noting the fire behind him.
GIT Base also depicts the fire but does not recognize Batman explicitly. GIT Large falls short in describing the background. And while BLIP identifies Batman, it simplifies the background too much as just "orange."
Winner: ViT+GPT2
Test Image 3: Boy Holding Banana
In our third test image, we have a simple photo of a young boy holding a banana up to his mouth, perhaps in mid-bite. Let‘s check the output:
GIT Base: A little boy holding a banana up near his mouth.
GIT Large: A little boy holding a banana.
BLIP: A little boy holding a banana to his mouth.
ViT+GPT2: A young boy holding a banana up to take a bite.
All models accurately characterize the key elements of this image – a boy with a banana. GIT Base and BLIP closely mimic each other with on-target captions. Meanwhile, ViT+GPT2 demonstrates slightly more creativity by generating the additional detail about the boy going to take a bite. But any would suffice.
Winner: Tie
Test Image 4: Tifa from Final Fantasy Game
Shifting gears to a video game character, this image depicts Tifa from Final Fantasy VII. She‘s recognizable by her black leather outfit. Let‘s see what captions the AI produced:
GIT Base: A woman with long black hair wearing a black leather jacket and skirt standing in front of a forest.
GIT Large: A woman standing in front of trees wearing black.
BLIP: A woman with long black hair wearing a black leather jacket and skirt, standing outside in front of trees.
ViT+GPT2: A woman with long dark hair, wearing black, standing among trees.
BLIP provides the most accurately descriptive caption for this video game image, correctly identifying key details like the black leather jacket and forest. GIT Large is far too vague, while ViT+GPT2 fails to capture specifics like the skirt or jacket type.
Winner: BLIP
Test Image 5: Time Magazine Cover
For our next test, we use a recent Time Magazine cover depicting Elon Musk as the 2021 Person of the Year.
GIT Base: A Time Magazine cover featuring a picture of Elon Musk. The headline reads "Person of the Year 2021: Elon Musk."
GIT Large: A Time Magazine cover with Elon Musk‘s face on it. It says person of the year 2021.
BLIP: A Time Magazine cover featuring Elon Musk‘s face and naming him Person of the Year for 2021.
ViT+GPT2: A Time Magazine cover with a photo of Elon Musk on it. The headline says "2021 Person of the Year" and Elon Musk‘s name.
In this case, GIT Base provides the most detailed and accurate description of the magazine cover, properly conveying Elon Musk as the 2021 Person of the Year.
BLIP misses a few finer points while the other captions lack specifics around the accompanying text. GIT Base demonstrates its ability to read and summarize printed words alongside analyzing image contents.
Winner: GIT Base
Test Image 6: Distracted Boyfriend Meme
For something more internet-culture related, our next image is the popular "distracted boyfriend" meme. It depicts a man checking out another woman while his upset girlfriend looks on.
GIT Base: A man looking at a woman walking by while another woman behind him looks angry.
GIT Large: A man standing with a woman and looking back at another passing woman.
BLIP: A man checking out another woman walking by while the woman next to him looks on unhappily.
ViT+GPT2: A man turning around to look at a woman walking past while ignoring the annoyed woman standing next to him.
The ViT+GPT2 caption most accurately conveys the meme imagery, properly capturing annoyed reaction of the man‘s partner. GIT Base is decent, minus the anger characterization. BLIP does alright as well in hitting the key elements. Meanwhile GIT Large oversimplifies the relationships and reactions.
Overall ViT+GPT2 seems most adept at interpreting not just image contents but the deeper cultural meaning, which is essential for meme images.
Winner: ViT+GPT2
Test Image 7: Man with Beard and Glasses
This test image provides a close-up portrait of a man with glasses and a beard to evaluate precision of facial feature recognition:
GIT Base: A close up of a man with a beard, mustache and black framed glasses looking into the camera.
GIT Large: Bearded man wearing black eyeglasses.
BLIP: A man with a beard, glasses and green eyes looking at the camera.
ViT+GPT2: A man with facial hair and black eyeglasses looking directly at the camera.
In inspecting facial details, GIT Base comes out ahead with its specificity around the beard type, glasses frame color, gaze direction and closeup perspective. BLIP incorrectly notes green eyes. And GIT Large and ViT+GPT2 both lack details that would complete the depictive caption.
Winner: GIT Base
Test Image 8: Woman Resting Eyes
If recognizing open vs closed eyes seems too challenging, did these image captioning models prove that assumption wrong? This photo shows a woman resting with her eyes shut:
GIT Base: A woman with her eyes closed resting her head against a wall.
GIT Large: A woman leaning on a wall with her eyes shut.
BLIP: A woman resting against a wall with her eyes closed.
ViT+GPT2: A woman leaning back on a wall and resting with her eyes shut.
All models accurately assess the woman‘s pose, state of eyes, and resting context – an encouraging sign. But once again, small details set GIT Base apart as it notes the precision of her head resting directly on the wall behind her. Subtle but descriptive all the same.
Winner: GIT Base
Evaluating Overall Performance
Having explored the image caption outputs across a diverse range of test images, how do the models stack up on key evaluation criteria:
Accuracy – GIT Base demonstrates the most reliably precise captioning of salient image details.
Descriptiveness – Tie between GIT Base and ViT+GPT2 which provide richest captions.
Context – ViT+GPT2 conveys most cultural meaning and information essential for full comprehension.
Overall – GIT Base is the top performer in core image understanding, though ViT+GPT2 handles abstract images best. BLIP also strong for human subjects and backgrounds.
Interestingly, GIT Large often lacked the level of detail compared to GIT Base – indicating that sometimes less is more with model architectures geared toward specificity. And living up to its lightweight design, ViT+GPT2 packs impressive contextual punch.
Tips for Improving Image Captioning
Based on evaluating these models and their performance across diverse test images, here are some tips on how to further enhance image captioning:
Split Up Complex Images
For images with multiple subjects or scene elements, split up the photo and generate separate captions which are then combined into an overall description. This divides-and-conquers to improve localization accuracy.
Try Multiple Models
Leverage the strengths of different models – detail from GIT Base, context from ViT+GPT2 and human analysis from BLIP. Merge outputs for best possible blend.
Provide Context
Supply any metadata, tags or context about images which models can incorporate instead of just raw pixels alone. This guides better cultural, geographic and conceptual understanding.
Key Takeaways
When comparing leading AI image captioning models GIT, BLIP and ViT+GPT2 analyzing real-world photos, key conclusions emerge:
-
GIT Base provides most accurate and descriptive captions due to architectural optimization for specificity.
-
ViT+GPT2 offers surprisingly detailed captions given compact size plus excellent interpretive context.
-
BLIP delivers strong identification of human subjects along with environmental backgrounds.
-
Ensembling multiple models and dividing complex images improve overall caption quality.
As image captioning continues to rapidly progress in AI, having insight on model design decisions and performance tradeoffs will be key to leveraging these tools. Whether you need highly precise descriptions or creative contextual captions, this guide covers the capabilities to watch out for.