TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification (2312.14149v4)

Abstract: The crux of learning vision-LLMs is to extract semantically aligned information from visual and linguistic data. Existing attempts usually face the problem of coarse alignment, e.g., the vision encoder struggles in localizing an attribute-specified object. In this work, we propose an embarrassingly simple approach to better align image and text features with no need of additional data formats other than image-text pairs. Concretely, given an image and its paired text, we manage to parse objects (e.g., cat) and attributes (e.g., black) from the description, which are highly likely to exist in the image. It is noteworthy that the parsing pipeline is fully automatic and thus enjoys good scalability. With these parsed semantics as supervision signals, we can complement the commonly used image-text contrastive loss with the multi-tag classification loss. Extensive experimental results on a broad suite of semantic segmentation datasets substantiate the average 5.2\% improvement of our framework over existing alternatives. Furthermore, the visualization results indicate that attribute supervision makes vision-LLMs accurately localize attribute-specified objects. Project page can be found at https://qinying-liu.github.io/Tag-Align.

• The paper introduces TagAlign, which enhances vision-language model alignment by integrating a multi-tag classification loss with standard image-text contrastive training.
• It employs an automated text parsing method using large language models to extract key object and attribute tags from descriptive texts.
• Extensive experiments show that TagAlign improves semantic segmentation performance by 3.65% compared to baseline models.

Understanding TagAlign: A Simple Approach to Precise Vision-LLM Alignment

In the field of AI, vision-LLMs like CLIP have become incredibly adept at interpreting images and texts. Yet, these models sometimes face challenges with aligning features precisely, such as focusing on an object described with specific attributes. To address this, a new framework called TagAlign offers an elegant solution without demanding additional data.

Tag Parsing with LLMs

At the core of TagAlign is an automated text parsing procedure that employs a LLM. Given any image with a descriptive text, the model identifies objects and attributes within the text—important elements that are also likely to be present in the image. This automatic parsing is beneficial for its high-quality output and scalability.

Multi-Tag Classification

TagAlign uses the tags identified from the parsed text as a form of supervision. By integrating a multi-tag classification loss into the model training, alongside the usual image-text contrastive loss, the system becomes equipped to accurately localize objects specified in the text—effectively aligning both image and text features.

Impressive Experimental Results

Experiments have shown that TagAlign improves average performance on a variety of semantic segmentation datasets by 3.65% over existing methods. Even when evaluating on datasets with or without a background class and using just the Conceptual 12M dataset for training, TagAlign demonstrates superior performance.

Visualizing TagAlign's Efficacy

Through weighted similarity maps, one can visually discern how TagAlign focuses on relevant regions in the image as per the text description. Unlike the baseline model CLIP, which can sometimes highlight the background, TagAlign zooms in on specific objects, showcasing the enhanced precision of this methodology.

The Big Picture

The introduction of TagAlign marks a significant step forward in perfecting the alignment process within vision-LLMs. It does so by cleverly leveraging the advancements in language processing to extract semantics from text and guiding the visual model towards a comprehensive understanding of the described elements. The ability to draw such detailed relations from only image-text pairs is a testament to the power of effective model design and highlights the potential of AI systems that learn more from less. Whether it's integrating attributes into understanding or enhancing textual comprehension, TagAlign expands the capabilities of vision-language embedding models, paving the way for more robust and intricate systems that can deal with a wide array of real-world applications.