Augmented Visual-Language Representation Learning with MLLMs

المفاهيم الأساسية

MLLMs enhance visual-language representation learning by extending diverse captions for each image and addressing issues of hallucinations and monotonous language styles.

الملخص

Visual-language pre-training success relies on large-scale datasets, but noisy pairs affect learning. MLLMs improve image-text associations without extra training cost. Text shearing maintains caption quality. Experiments show significant performance improvements in zero-shot and fine-tuning settings. Single model rewriting has limitations compared to multiple MLLMs. Training factors like batch size, epochs, and caption length impact performance.

الإحصائيات

Our method consistently obtains 5.6 ∼ 35.0% and 16.8 ∼ 46.1% improvement on Recall@1 under the fine-tuning and zero-shot settings, respectively.
The average improvement of our method is 13.4 on 15 datasets and 13.1 on ImageNet.
For zero-shot retrieval on MSCOCO, the R@1 of TR and IR increase by 27.2 and 19.4, respectively.
Our method achieves an improvement of 16.8∼23.4 when using BLIP for zero-shot retrieval.
Our method demonstrates an average improvement of 7.9 on linear probing across common datasets.

اقتباسات

"We demonstrate that Multi-modal Large Language Models (MLLMs) can enhance visual-language representation learning by establishing richer image-text associations for image-text datasets."
"Our approach exhibits the following characteristics: It is compatible with multiple visual-language pre-training frameworks like CLIP and BLIP, demonstrating significant performance improvements across various downstream tasks without introducing additional training overhead."
"Most recent works demonstrate that LLM and MLLM can be used as re-writers to improve caption quality without reducing the number of training pairs."

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 does the use of multiple MLLMs impact the diversity and accuracy of extended captions compared to using a single model

Using multiple MLLMs has a significant impact on the diversity and accuracy of extended captions compared to relying on a single model. Each MLLM has its unique language style, focus, and strengths based on its training data and architecture. By leveraging multiple MLLMs, we can capture a broader range of linguistic concepts, vocabulary choices, and sentence structures. This diversity enriches the pool of extended captions for each image, providing varied perspectives and interpretations.
Moreover, different MLLMs may excel in generating captions for specific types of images or scenes due to their specialized training objectives or pre-training datasets. For instance, one MLLM might be more adept at describing complex visual details while another excels in capturing emotional nuances or contextual information. By combining these diverse outputs from multiple models, we create a more comprehensive set of extended captions that cover various aspects of an image's content.
In terms of accuracy, utilizing multiple MLLMs helps mitigate the risk of bias or limitations inherent in individual models. Each model contributes its unique understanding and interpretation to the caption generation process. Through ensemble learning techniques that leverage the collective intelligence of diverse models, we can enhance the overall quality and reliability of extended captions by cross-validating against different sources.

What potential biases or limitations may arise from relying solely on synthetic captions generated by MLLMs for visual-language representation learning

Relying solely on synthetic captions generated by MLLMs for visual-language representation learning introduces potential biases and limitations that need to be carefully addressed. One primary concern is related to hallucinations or inaccuracies in the generated text due to inherent biases present in individual models' training data or architectures.
MLLMs may inadvertently introduce monotonous language styles or repetitive patterns into synthetic captions if not properly controlled during generation. These biases can lead to overfitting on certain textual features common across all generated captions rather than accurately reflecting diverse semantic associations with images.
Furthermore, there is a risk of semantic drift or concept shift when using only synthetic captions without human annotation feedback loops during training. Human annotators provide valuable context-specific insights that help refine and validate machine-generated text against real-world scenarios.
Additionally, relying solely on synthetic captions may limit the model's ability to adapt flexibly to new unseen data instances outside the scope covered during pre-training with fixed sets of generated texts.

How might incorporating human annotation or feedback during the text shearing process further enhance the quality of extended captions

Incorporating human annotation or feedback during the text shearing process can significantly enhance the quality of extended captions by providing valuable insights into linguistic nuances,
contextual relevance,
and domain-specific knowledge.
Human annotators possess cognitive abilities such as common-sense reasoning,
world knowledge,
and cultural awareness
that complement machine-generated descriptions.
Their input can help identify errors,
ambiguities,
or inconsistencies in synthetic texts produced by MLLMs
and offer corrections
or alternative suggestions for improving clarity
and coherence.
By integrating human feedback into text shearing procedures,
we introduce an additional layer
of validation
to ensure that extended captions maintain high standards
of accuracy
diversity.
This iterative refinement process involving both machines
humans fosters collaborative learning environments where AI systems continuously evolve through interactions with human experts
real-world users.
Ultimately,
this hybrid approach leverages
complementary strengths
capabilities
machines humans towards achieving optimal performance outcomes
visual-language representation tasks