FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models (2405.10286v1)

Abstract: Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically, we firstly study and analyze two issues affecting training: incorrect assignment of negative pairs, and low caption quality and diversity. Then, we devise effective solutions for addressing both problems, which essentially require training with multiple true positive pairs. Finally, we propose training with sigmoid loss to address such a requirement. We show very large gains over the current state-of-the-art for both image recognition ($\sim +6\%$ on average over 11 datasets) and image retrieval ($\sim +19\%$ on Flickr30k and $\sim +15\%$ on MSCOCO).

• The paper introduces dynamic correction of negative pair assignments by adjusting image-text, image-image, and text-text similarities to improve pre-training.
• It enriches training data with multiple pseudo-captions from BLIP2, enhancing caption diversity and quality in noisy web datasets.
• Using a robust sigmoid loss, the method yields up to 6% recognition improvements and 19% retrieval gains on benchmark datasets.

Essay on "FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-LLMs"

The paper "FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-LLMs" by Bulat, Ouali, and Tzimiropoulos presents a meticulous examination of two crucial impediments in vision-language contrastive pre-training: the erroneous assignment of negative pairs and the low quality and diversity of captions. The authors propose innovative solutions to these problems, achieving significant performance improvements in image recognition and retrieval tasks.

Key Contributions

1. Analysis of Flaws in Pre-training Data

The paper begins with an in-depth analysis of the common pitfalls in vision-language contrastive pre-training, particularly focusing on noise and caption quality. The authors identify two primary issues: the incorrect assignment of negative pairs due to the presence of near-duplicate samples and the low quality and diversity of captions extracted from web-collected datasets. These issues, while recognized in previous literature, have not been fully addressed.

2. Correcting Negative Pair Assignments

To address the incorrect assignment of negative pairs, the authors propose an algorithm that dynamically adjusts negative assignments by considering image-text, image-image, and text-text similarities. This multi-faceted approach ensures that semantically similar pairs that are incorrectly treated as negatives are re-assigned as positives. This on-the-fly correction is a significant improvement over traditional methods that statically assign negatives without accounting for semantic similarities.

3. Improving Caption Quality and Diversity

The authors tackle the problem of low caption quality and diversity by generating multiple pseudo-captions for each image using the state-of-the-art captioning model, BLIP2. These synthetic captions are used to augment the training batches, providing a richer and more diverse set of positive samples. This approach effectively mitigates the impact of noisy and repetitive captions that are prevalent in web-collected datasets.

4. Use of Sigmoid Loss for Training

Given the requirement to handle a variable number of positive pairs per image, the authors propose training the model using a sigmoid loss, which is more robust to noise compared to the traditional contrastive loss. This adaptation allows the model to dynamically adjust to the varying number of positives and enhances its robustness against errors in the mining process.

Numerical Results

The proposed method demonstrates substantial improvements over the state-of-the-art across multiple benchmark datasets. For image recognition, the authors report an average gain of approximately 6% over 11 datasets, with image retrieval performance improving by 19% and 15% on Flickr30k and MSCOCO, respectively. These numerical results underscore the efficacy of the proposed solutions in enhancing vision-language representations.

Analysis of Theoretical and Practical Implications

The introduction of dynamic negative pair correction and enhanced caption generation techniques holds significant theoretical and practical implications. Theoretically, the work advances the understanding of how semantic similarities can be leveraged to improve contrastive learning. Practically, the proposed methods can be integrated into existing vision-LLMs to achieve higher accuracy with minimal additional computational cost. By alleviating the noise inherent in web-collected datasets, the proposed solutions pave the way for more robust and generalizable vision-LLMs.

Future Directions

The findings presented in this paper open several avenues for future research. One potential direction is the exploration of alternative caption generation models and their impact on the diversity and quality of training data. Additionally, further investigation into the scalability of the proposed methods across larger and more diverse datasets could provide deeper insights into their generalizability. Finally, exploring the integration of these techniques with more advanced architectures, such as transformers, could yield even more potent vision-LLMs.

In conclusion, the paper by Bulat, Ouali, and Tzimiropoulos makes a substantial contribution to the field of vision-language pre-training. By addressing the critical issues of negative pair assignment and caption quality, the authors achieve significant performance gains, demonstrating the potential of their methods to advance the state of the art in image recognition and retrieval tasks. The proposed solutions are both theoretically sound and practically viable, offering a robust framework for building stronger vision-LLMs.