Investigating the Modality Gap and Object Bias in Contrastive Vision-Language Representation Learning

Core Concepts

The modality gap, a separation of image and text embeddings in the shared representation space, and the bias towards objects over other factors, such as attributes, are two key challenges in contrastive vision-language representation learning. The driving factor behind both phenomena is the information imbalance between images and their captions.

Abstract

The paper investigates two key phenomena in contrastive vision-language representation learning: the modality gap and the bias towards objects. Key highlights: The modality gap is driven by only a few embedding dimensions, and closing the gap does not necessarily improve downstream performance. While a larger modality gap positively correlates with downstream performance, this is likely due to common confounding factors rather than a causal relationship. Image and text embeddings exhibit different biases and neighborhood orderings, suggesting they encode information differently. The authors propose a measure for object bias and find that contrastive vision-language models are indeed biased towards objects. However, improvements on object tasks also lead to improvements on attribute tasks. The common trigger for both the modality gap and object bias is the information imbalance between images and their captions. Reducing this imbalance mitigates both phenomena and improves downstream performance.

Stats

"Few embedding dimensions drive the modality gap and two dimensions suffice to separate the modalities." (Section 4.1) "A larger modality gap positively correlates with downstream performance, yet there is no indication that this is a causal relationship, but there are rather common confounders." (Section 4.2) "Contrastive vision-language models trained on large-scale data tend to have a lower object bias than medium-scale models." (Section 5) "Reducing the level of information imbalance causes a smaller modality gap and a smaller object bias." (Section 6)

Quotes

"Few embedding dimensions drive the modality gap and two dimensions suffice to separate the modalities." "A larger modality gap positively correlates with downstream performance, yet there is no indication that this is a causal relationship, but there are rather common confounders." "Contrastive vision-language models trained on large-scale data tend to have a lower object bias than medium-scale models." "Reducing the level of information imbalance causes a smaller modality gap and a smaller object bias."

Key Insights Distilled From

Two Effects, One Trigger

by Simon Schrod... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07983.pdf

Deeper Inquiries

What other factors, beyond dataset size and quality, might influence the modality gap and object bias in contrastive vision-language models

In addition to dataset size and quality, several other factors can influence the modality gap and object bias in contrastive vision-language models. One crucial factor is the architecture and design of the model itself. The choice of the encoder architecture, the number of layers, the dimensionality of the embeddings, and the training objectives can all impact the learned representations. For example, using more complex or deeper architectures may help capture a wider range of features and reduce biases towards specific concepts. Additionally, the choice of contrastive loss function and hyperparameters can also play a significant role in shaping the learned representations. Another factor to consider is the diversity and representativeness of the training data. Biases in the training data, such as over-representation of certain concepts or under-representation of others, can lead to biases in the learned representations. Ensuring a diverse and balanced dataset can help mitigate these biases and improve the overall performance of the model. Furthermore, the pre-processing and augmentation techniques applied to the data can impact the modality gap and object bias. Techniques such as data augmentation, normalization, and feature selection can influence how well the model captures different concepts and reduces biases towards specific objects or attributes.

How can we design contrastive vision-language models that are less biased towards objects and better capture other latent factors, such as attributes

To design contrastive vision-language models that are less biased towards objects and better capture other latent factors, such as attributes, several strategies can be employed: Balanced Training Data: Ensure that the training data is balanced and representative of the full range of concepts and attributes the model is expected to learn. This can help prevent biases towards specific objects and promote a more comprehensive understanding of the data. Multi-Task Learning: Incorporate multi-task learning objectives that explicitly require the model to capture a diverse set of concepts and attributes. By training the model on multiple related tasks simultaneously, it can learn to generalize better across different types of information. Regularization Techniques: Use regularization techniques such as dropout, weight decay, or batch normalization to prevent the model from overfitting to specific objects or concepts. Regularization can help promote a more balanced representation of all latent factors. Enriched Captions: Provide richer and more detailed captions during training that describe a wider range of attributes and relationships in the images. This can help the model learn to focus on a broader set of features and reduce biases towards specific objects. Fine-Tuning and Transfer Learning: Fine-tune the model on specific attribute detection tasks or use transfer learning from models trained on attribute-rich datasets to improve the model's ability to capture attributes.

How do the findings in this paper apply to other multimodal representation learning approaches beyond contrastive vision-language models

The findings in this paper can be applied to other multimodal representation learning approaches beyond contrastive vision-language models. The insights gained about the impact of information imbalance, modality gap, and object bias are relevant to any model that learns representations from multiple modalities, such as image-text models, audio-visual models, or text-image models. For example, in audio-visual models, ensuring a balance between the information available in the audio and visual modalities can help improve the overall performance and reduce biases towards specific sounds or visual features. Similarly, in text-image models, addressing biases towards certain words or objects can lead to more comprehensive and accurate representations. By considering the factors influencing the modality gap and object bias identified in this paper, researchers can design more robust and unbiased multimodal representation learning models across a variety of domains and applications.