LoTLIP: Improving Language-Image Pre-training for Long Text Understanding (2410.05249v5)

Abstract: Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key reason causing such an issue is that the training images are usually paired with short captions, leaving certain tokens easily overshadowed by salient tokens. Towards this problem, our initial attempt is to relabel the data with long captions, however, directly learning with which may lead to performance degradation in understanding short text (e.g., in the image classification task). Then, after incorporating corner tokens to aggregate diverse textual information, we manage to help the model catch up to its original level of short text understanding yet greatly enhance its capability of long text understanding. We further look into whether the model can continuously benefit from longer captions and notice a clear trade-off between the performance and the efficiency. Finally, we validate the effectiveness of our approach using a self-constructed large-scale dataset, which consists of 100M long caption oriented text-image pairs. Our method demonstrates superior performance in long-text-image retrieval tasks. The project page is available at https://wuw2019.github.io/lot-lip.

• The paper introduces a large-scale dataset of 100 million text-image pairs with extended captions to improve long text understanding.
• The study proposes a novel corner token mechanism that enriches text feature extraction, balancing comprehension of both short and long texts.
• Experimental results reveal an 11.1% improvement in long-text image retrieval tasks, highlighting its impact on multimodal applications.

Overview of LoTLIP: Improving Language-Image Pre-training for Long Text Understanding

The paper "LoTLIP: Improving Language-Image Pre-training for Long Text Understanding" addresses significant challenges inherent in language-image pre-training (LIP) models when interacting with long texts. The authors identify a critical limitation in existing datasets, which predominantly associate images with short captions. This practice results in certain tokens being overshadowed by more salient ones, thus hindering the model's capability to comprehend long textual inputs effectively.

Key Contributions

1. Dataset Expansion: The authors construct a large-scale dataset, consisting of 100 million text-image pairs annotated with long captions. This new dataset aims to bridge the gap in training data, offering models the opportunity to learn from extended textual information.
2. Methodology - Corner Tokens: The paper introduces corner tokens into the text encoder, which aids in gathering diverse text features. This mechanism enhances the model's ability to regain proficiency in short text understanding while simultaneously boosting its capacity for longer text comprehension.
3. Performance Evaluation: Utilizing the innovative dataset, the authors demonstrate a notable 11.1% improvement in long-text image retrieval tasks over existing models like Long-CLIP. This performance is measured against various benchmarks, illustrating the model's balanced capability in handling both long and short textual contexts.

Experimental Results

The authors meticulously examine the impact of sub-caption numbers and token limitations on model performance. The integration of long captions notably enhances the model’s proficiency in long-text image retrieval, with minor trade-offs in short-text tasks. By optimizing the number of tokens and corner token strategies, the LoTLIP model achieves balanced improvements across several evaluation tasks, including image classification and text-image retrieval.

Implications and Future Work

This study has substantial implications for improving multi-modality models, especially in applications requiring nuanced text-image alignment, such as advanced content-based image retrieval systems and enhanced visual question answering systems.

The release of the long-text dataset alongside the model and code further facilitates reproducibility and exploration in LIP research. Future developments could consider optimizing corner token utilization and exploring the balance between training efficiency and model complexity. There is also potential for expanding into more diverse forms of multimodal data to ascertain the adaptability of the methodology in varied contexts.

Conclusion

The paper presents a robust approach to improving text comprehension in LIP models, underpinned by a carefully constructed dataset and an innovative encoding strategy. The results advocate for a paradigm shift in model training towards longer, more descriptive textual inputs, thereby opening new avenues for research and application in artificial intelligence and multi-modality learning.