Semi-Supervised Image Captioning with CLIP
Authors: Chuanyang Jin
Abstract: Image captioning, a fundamental task in vision-language understanding, seeks to generate accurate natural language descriptions for provided images. The CLIP model, with its rich semantic features learned from a large corpus of image-text pairs, is well-suited for this task. In this paper, we present a two-stage semi-supervised image captioning approach that exploits the potential of CLIP encoding. Our model comprises a CLIP visual encoder, a mapping network, and a language model for text generation. In the initial stage, we train the model using a small labeled dataset by contrasting the generated captions with the ground truth captions. In the subsequent stage, we continue the training using unlabeled images, aiming to maximize the image-caption similarity based on CLIP embeddings. Remarkably, despite utilizing less than 2% of the COCO-captions, our approach delivers a performance comparable to state-of-the-art models trained on the complete dataset. Furthermore, the captions generated by our approach are more distinctive, informative, and in line with human preference.
What, Why and How
Here is a summary of the key points from the paper:
What:
-
The paper proposes a semi-supervised image captioning approach that utilizes CLIP embeddings and objectives.
-
The model has 3 main components: CLIP visual encoder, mapping network, GPT language model.
-
A 2-stage training process is used:
- Supervised stage: Train on small labeled dataset by comparing generated captions to reference captions.
- Unsupervised stage: Continue training on unlabeled images by maximizing image-caption similarity based on CLIP.
Why:
-
Image captioning models typically require large labeled datasets and are limited by the quality of reference captions.
-
Leveraging CLIP allows reducing reliance on labeled data and generates more descriptive, human-like captions.
How:
-
CLIP encodes image into embedding, mapping network converts it to prefix input for GPT.
-
Supervised stage trains with cross-entropy loss between generated and reference captions.
-
Unsupervised loss maximizes image-caption similarity based on CLIP embeddings using Gumbel-Softmax.
-
Only 10,000 labeled images needed for initial supervised stage vs 1M+ typically.
-
CLIP score used for evaluation rather than just reference-based metrics.
In summary, the paper presents a semi-supervised approach for image captioning that trains initially on a small labeled dataset and then leverages CLIP embeddings and similarity for unsupervised learning to generate more informative captions while drastically reducing data dependence.
Main Contributions
Here are the main contributions of this paper:
-
Proposes a semi-supervised learning framework for image captioning that significantly reduces reliance on large labeled datasets.
-
Leverages CLIP embeddings and image-text similarity objectives for unsupervised training after minimal supervised pre-training.
-
Achieves strong performance using only 10,000 labeled images (less than 2% of COCO dataset) compared to 1M+ typically needed.
-
Generates more descriptive, distinctive and human-like captions compared to reference similarity-based methods.
-
Introduces integrated supervised and unsupervised training within a unified framework that can be inter-switched.
-
Employs Gumbel-Softmax technique to enable differentiable sampling for discrete text generation.
-
Evaluates using CLIP score rather than just reference-based metrics to account for human preferences.
-
Comprehensive experiments analyzing impact of supervised pre-training, comparing model variations, and evaluating against state-of-the-art methods.
-
Provides implementation details to support reproducibility.
In summary, the key contributions include the semi-supervised approach, use of CLIP objectives, unified training framework, Gumbel-Softmax method, and CLIP-based evaluation, resulting in a highly data-efficient model that generates more informative and human-like captions.
Method Section
Here is a summary of the method section from the paper:
The paper proposes a semi-supervised learning framework with two stages:
Supervised Stage:
- Uses a small labeled dataset of 10,000 images (<2% of COCO) for initial training.
- Model has 3 components: CLIP encoder, mapping network, GPT decoder.
- Mapping network converts CLIP embeddings to GPT input prefixes.
- Trains by comparing generated captions to reference captions.
- Uses cross-entropy loss between predicted and ground truth next token.
Unsupervised Stage:
- Continues training on unlabeled images.
- Generates candidate captions for each image.
- Defines loss as negative of image-caption similarity based on CLIP.
- Seeks to maximize cosine similarity of image and text embeddings.
- Uses Gumbel-Softmax for differentiable sampling from text generation distribution.
- Refines model for ~10 epochs on unlabeled data.
The key aspects are: 1) Minimal supervised pre-training, 2) Leveraging CLIP similarity for unsupervised optimization, 3) Integrated framework switching between supervised and unsupervised.
This semi-supervised approach with CLIP objectives significantly reduces reliance on labeled data while improving caption quality.
High-Level Pseudo Code
Here is the high-level pseudo code for the semi-supervised image captioning method:
# Model Components
clip_encoder = CLIPEncoder()
mapping_network = MappingNetwork()
gpt_decoder = GPTDecoder()
# Supervised Stage
labeled_data = get_small_labeled_dataset(num_images=10000)
for epoch in num_supervised_epochs:
for (image, reference_captions) in labeled_data:
image_embedding = clip_encoder(image)
input_prefixes = mapping_network(image_embedding)
generated_captions = gpt_decoder(input_prefixes)
loss = caption_cross_entropy(generated_captions, reference_captions)
update_model(loss)
# Unsupervised Stage
unlabeled_data = get_unlabeled_images()
for epoch in num_unsupervised_epochs:
for image in unlabeled_data:
image_embedding = clip_encoder(image)
input_prefixes = mapping_network(image_embedding)
candidate_captions = gpt_decoder.sample(input_prefixes)
caption_embeddings = clip_encoder(candidate_captions)
image_caption_similarity = cosine_similarity(image_embedding, caption_embeddings)
loss = -image_caption_similarity
update_model(loss)The key aspects are:
- Minimal supervised pre-training
- Unsupervised fine-tuning with CLIP similarity objectives
- Integrated framework switching between supervised and unsupervised stages
Detailed Pseudo Code
Here is a more detailed pseudo code implementation of the semi-supervised image captioning method:
# Model Components
clip = CLIP() # pretrained CLIP
gpt = GPT() # pretrained GPT
mlp = MLP(in_dim=512, out_dim=10*512) # mapping network
# Supervised Training
labeled_data = load_subset_of_coco(num_images=10000)
for epoch in range(num_supervised_epochs):
for batch in labeled_data:
images, reference_captions = batch
# Forward Pass
image_embeddings = clip.encode_image(images)
input_prefixes = mlp(image_embeddings)
generated_captions = gpt.generate(input_prefixes)
reference_embeddings = clip.encode_text(reference_captions)
# Loss
cross_entropy_loss = caption_cross_entropy(generated_captions, reference_embeddings)
# Backward Pass
cross_entropy_loss.backward()
optimizer.step()
optimizer.zero_grad()
# Unsupervised Training
unlabeled_data = load_rest_of_coco_images()
for epoch in range(num_unsupervised_epochs):
for batch in unlabeled_data:
images = batch
batch_size = images.shape[0]
# Forward Pass
image_embeddings = clip.encode_image(images)
input_prefixes = mlp(image_embeddings)
sampled_captions = gpt.sample(input_prefixes,
num_samples=5*batch_size)
generated_caption_embeddings = clip.encode_text(sampled_captions)
image_caption_similarities = cosine_similarity(image_embeddings,
generated_caption_embeddings)
# Loss
clip_similarities = torch.mean(image_caption_similarities, dim=1)
unsupervised_loss = -torch.mean(clip_similarities)
# Backward Pass
unsupervised_loss.backward()
optimizer.step()
optimizer.zero_grad()Let me know if any part of the pseudo code needs further explanation!