Source-linked AI summary
ClipCap: CLIP Prefix for Image Captioning
Ron Mokady, Amir Hertz, Amit H. Bermano
TL;DR
Image captioning systems often require substantial training resources, parameters, data, or annotations. ClipCap uses a CLIP-derived prefix with GPT-2 through a mapping network, including a variant that freezes both pretrained models. It produces meaningful captions with comparable state-of-the-art results on challenging datasets while remaining simpler, faster, and lighter.
Problem
Existing image captioning approaches often require extensive training, many trainable parameters, large datasets, and sometimes additional annotations.
Method
ClipCap maps CLIP image embeddings into a caption prefix for GPT-2, with a lightweight variant training only the mapping network while CLIP and GPT-2 remain frozen.
Results
ClipCap achieves comparable results to state-of-the-art methods on Conceptual Captions and nocaps while requiring less training time and fewer trainable parameters.
Takeaways & Limitations
The approach provides a simple captioning model that uses pretrained visual and textual models without additional annotations.
Takeaways & Limitations
The model still suffers from data bias and would benefit from improved CLIP object-detection ability.
Abstract
from arXiv · showhide
Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image. In this paper, we present a simple approach to address this task. We use CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. The recently proposed CLIP model contains rich semantic features which were trained with textual context, making it best for vision-language perception. Our key idea is that together with a pre-trained language model (GPT2), we obtain a wide understanding of both visual and textual data. Hence, our approach only requires rather quick training to produce a competent captioning model. Without additional annotations or pre-training, it efficiently generates meaningful captions for large-scale and diverse datasets. Surprisingly, our method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen, allowing a lighter architecture with less trainable parameters. Through quantitative evaluation, we demonstrate our model achieves comparable results to state-of-the-art methods on the challenging Conceptual Captions and nocaps datasets, while it is simpler, faster, and lighter. Our code is available in https://github.com/rmokady/CLIP_prefix_caption.
1. Introduction
ClipCap addresses image captioning by connecting CLIP’s visual-language representations to GPT-2, targeting simpler and faster training with fewer trainable parameters. The approach produces meaningful captions and achieves results comparable to state-of-the-art methods on challenging datasets.
- Motivation: Image captioning requires semantic understanding and choosing among many valid descriptions shaped by the training dataset.The challenges range from recognizing objects to understanding relations between image regions.
- Motivation: Existing captioning systems bridge visual and textual representations but often require extensive training, many parameters, large datasets, and additional annotations.These requirements limit practical applicability and make the models resource hungry.
- Motivation: Faster training and fewer trainable parameters are especially useful when models must be trained repeatedly across datasets or updated with fresh samples.The paper motivates lightweight captioning for different users, applications, and routinely arriving data.
- Method: ClipCap applies a mapping network to CLIP embeddings to create a fixed-size prefix that is concatenated with caption embeddings for GPT-2.GPT-2 generates captions word by word from the CLIP-derived prefix.
- Method: A lighter variant trains only the mapping network while keeping CLIP and GPT-2 frozen, substantially reducing trainable parameters.The architecture uses a lightweight transformer-based mapping network, with an MLP-based alternative also proposed.
- Results: The model requires lower training time while producing meaningful captions and comparable results to state-of-the-art methods on Conceptual Captions and nocaps.The paper reports marginally lower results on the more restricted COCO benchmark.
2. Related Works
Related captioning systems commonly combine visual encoders with textual decoders, while newer approaches use shared vision-language representations and transformer-based language models. These methods can require object-detection features or additional supervision, motivating ClipCap’s use of CLIP representations.
- Vision-Language Pre-training: CLIP jointly represents images and text descriptions in a shared semantic latent space learned from more than 400 million image-text pairs.ClipCap uses CLIP’s visual encoder because the captioning input contains no text.
- Visual Encoding: Image captioning models typically encode image pixels into feature vectors before producing a sequence of words.Early systems used classification-network features, while later systems used object-detection features.
- Textual Decoding: Textual decoders evolved from LSTM variants to transformer architectures, with language models commonly pre-trained and then fine-tuned for specific tasks.Task-specific fine-tuning uses additional supervision.
- Vision-Language Pre-training: Several related vision-language captioning methods use visual tokens or object tags with BERT, requiring object-detector outputs or additional supervision.These requirements limit applicability across datasets where object-detection annotations are unavailable.
3. Method
ClipCap maps CLIP image embeddings into a prefix for a language model, which generates captions autoregressively. The method also explores fine-tuning or freezing the language model while training the mapping network.
- 3. Method: The approach addresses the representation gap between CLIP and the language model by using the CLIP embedding as a condition and prefix.The prefix supplies semantic information so an autoregressive language model can predict the next token without future tokens.
- 3.1. Overview: The method uses a mapping network to convert a CLIP image embedding into k embedding vectors that serve as a caption prefix.The mapped vectors have the same dimension as word embeddings and are concatenated with caption embeddings.
- 3.1. Overview: Caption generation is trained by predicting caption tokens autoregressively while conditioning on the visual prefix.The mapping component is optimized with cross-entropy loss, and inference predicts tokens sequentially from the prefix.
- 3.2. Language Model Fine-tuning: The paper considers variants that either fine-tune the language model with the mapping network or keep the language model fixed during training.Fine-tuning increases trainable parameters, whereas the frozen-language-model variant adjusts the model through a learned prefix.
- 3.3. Mapping Network Architecture: When the language model is fine-tuned, a simple single-hidden-layer MLP mapping network can produce realistic and meaningful captions.The mapping network translates the CLIP embedding into the GPT-2 representation space, aided by CLIP’s vision-language pre-training.
- 3.3. Mapping Network Architecture: For a frozen language model, the method uses a more expressive transformer mapping network that combines CLIP visual encoding with a learned constant input.The learned embeddings can lack textual meaning and instead be optimized to steer the fixed language model.
4. Results
The method achieves comparable captioning results on Conceptual Captions and nocaps with substantially lower training and parameter costs, while producing meaningful captions across datasets and new images. Performance depends on configuration: language-model fine-tuning helps on complex, stylistically varied data, whereas frozen-language-model variants can be lighter and competitive.
- Evaluation setup: The evaluation covers COCO-captions, nocaps, and Conceptual Captions, using object-detection-based systems including Oscar, VLP, and BUTD as baselines.nocaps measures generalization to unseen classes and concepts, while COCO is limited to 80 classes.
- Quantitative results: Conceptual Captions results surpass VLP while requiring orders of magnitude less training time.The lightweight frozen-GPT-2 variant performs worse on this dataset, which the authors associate with its broad variety of styles.
- Quantitative results: On nocaps, the method achieves results comparable to Oscar while using a fraction of the training time and trainable parameters without additional object tags.Oscar is slightly better on SPICE, while the proposed method is slightly better on CIDEr.
- Quantitative results: On COCO, the method is close to VLP and BUTD, while Oscar performs best using additional object tags; competing training-time comparisons exclude some pre-training costs.VLP pre-training over Conceptual Captions is reported to require 1200 GPU hours.
- Qualitative evaluation: Qualitative results show meaningful captions on COCO and Conceptual Captions, generalization to arbitrary and smartphone images, and recognition of some uncommon objects.The model also fails to recognize some objects, such as a bicycle beside a train, and can reproduce dataset-specific caption bias.
- Ablations and interpretability: Fine-tuning improves expressiveness on Conceptual Captions but can increase overfitting, while frozen-language-model variants are preferable when results are comparable.With longer prefixes, language-model tuning overfits the training set; frozen models improve on both training and test evaluations, subject to architecture and memory limits.
5. Conclusion
The CLIP-based captioning method is simple, requires no additional annotations, and trains faster while using a minimal mapping network. Its merit increases as datasets become richer and more diverse, because it adapts pretrained semantic understanding to the target dataset’s style.
- The method requires no additional annotations and is faster to train than conventional captioning approaches.
- The approach trains only a minimal mapping network while leveraging existing pretrained models.
- The method’s merit increases as the dataset becomes richer and more diverse.
- It adapts pretrained models’ semantic understanding to the target dataset’s style rather than learning new semantic entities.