MLLMs-Augmented Visual-Language Representation Learning: Enhancing Image-Text Associations with Multiple Large Language Models

المفاهيم الأساسية

MLLMs can enhance visual-language representation learning by extending diverse captions for each image, improving performance in various tasks.

الملخص

The content discusses the use of Multimodal Large Language Models (MLLMs) to enrich visual-language representation learning by extending multiple diverse captions for each image. The approach addresses issues like bias introduced by MLLMs' hallucinations and monotonous language styles. Results show significant improvements in zero-shot and fine-tuning settings for image-text retrieval and image classification tasks.

Structure:

1. Introduction to Visual-Language Pre-training Success
2. Limitations of Existing Datasets and Approaches
3. Proposed Method: Multi-view caption extractor and Text Shearing
4. Experiments and Results on Image-Text Retrieval, Image Classification, Vision Question Answering, Visual Reasoning, and Image Captioning
5. Ablation Study on Caption Length, Batch Size, Number of MLLMs, and Training Epochs
6. Visualization of Image Captioning Differences and Distribution Comparisons
7. Comparison with VeCLIP method

الإحصائيات

5.6 ∼ 35.0% improvement on Recall@1 under fine-tuning setting.
16.8 ∼ 46.1% improvement on Recall@1 under zero-shot setting.

اقتباسات

"Our method consistently obtains improvement on Recall@1 under both fine-tuning and zero-shot settings."
"Our approach exhibits characteristics compatible with various pre-training frameworks."

الرؤى الأساسية المستخلصة من

MLLMs-Augmented Visual-Language Representation Learning

by Yanqing Liu,... في arxiv.org 03-14-2024

https://arxiv.org/pdf/2311.18765.pdf

استفسارات أعمق

How can the bias introduced by MLLMs' hallucinations be further mitigated

MLLMs can introduce bias through hallucinations in generated captions. To further mitigate this bias, several strategies can be employed:

Diverse Model Ensemble: Instead of relying on a single MLLM for caption generation, using an ensemble of diverse models can help reduce the impact of individual model biases. By aggregating outputs from multiple models, the diversity in generated captions increases, potentially mitigating hallucination-induced biases.

Fine-tuning and Calibration: After generating extended captions, fine-tuning the pre-trained models on a smaller dataset containing human-annotated high-quality image-text pairs can help calibrate the model's language generation capabilities. This process aims to align the model's output with more accurate and reliable textual descriptions.

Adversarial Training: Introducing adversarial training techniques where synthetic captions are evaluated against ground truth annotations by adversarial networks can provide feedback to refine the captioning process and reduce hallucinations.

Regularization Techniques: Applying regularization methods such as dropout or weight decay during training can prevent overfitting and encourage generalization, reducing the likelihood of biased hallucinations in generated captions.

What are the implications of using multiple MLLMs for enhancing visual-language representation learning

Using multiple MLLMs for enhancing visual-language representation learning offers several implications:

Diversity in Captions: Different MLLMs have unique text structures and focus areas due to their varied training data and architectures. Leveraging multiple MLLMs enriches the dataset with diverse perspectives on image descriptions, leading to a broader range of semantic associations between images and texts.

Improved Generalization: By incorporating insights from various MLLMs during pre-training, models gain a more comprehensive understanding of visual concepts across different domains or datasets. This enhanced generalization ability allows for better performance on downstream tasks without extensive fine-tuning.

Robustness Against Biases: Utilizing multiple MLLMs helps counteract individual model biases that may arise during caption generation or representation learning processes. The collective knowledge from diverse models promotes robustness against specific biases present in any single model.

How does the proposed "text shearing" technique impact the quality of extended captions in comparison to other methods

The "text shearing" technique impacts the quality of extended captions by addressing issues related to excessive length while maintaining semantic relevance compared to other methods like direct concatenation or simple truncation:

Reduced Hallucinations: Text shearing limits the maximum token length allowed for generated captions, preventing excessive elaboration that often leads to irrelevant content or hallucinated information towards later parts of lengthy texts.

2..Semantic Preservation: By trimming extended captions down to match original lengths based on average annotation sizes rather than arbitrary cutoff points ensures essential details closest to images are retained while eliminating redundant information.
3..Quality Control: Unlike direct concatenation which might lead to disjointed narratives or abrupt endings due to varying lengths among different sources; text shearing maintains coherence throughout synthesized texts by focusing on complete clauses within set boundaries.
4..Bias Mitigation: Compared with simple truncation which risks losing crucial context at truncated ends; text shearing strikes a balance between retaining key semantics near beginnings while curbing potential distortions introduced by prolonged generations towards conclusions