Pre-trained Vision-Language Models Encode Discoverable Visual Concepts

Core Concepts

Pre-trained vision-language models can learn and encode a diverse set of generic and visually discriminative concepts that can be effectively utilized for interpretable and generalizable visual recognition.

Abstract

The paper investigates whether pre-trained vision-language models (VLMs) can learn and encode primitive visual concepts, such as color, texture, and shape, that can be leveraged for interpretable and generalizable visual recognition.
The authors first observe that previous works on extracting visual concepts from VLMs often rely on category-specific prompts, which can lead to "shortcuts" and biased conclusions about the models' true concept learning capabilities. To address this issue, the authors propose a new Concept Discovery and Learning (CDL) framework that discovers category-agnostic and visually discriminative concepts from a large image-caption dataset, leveraging both the visual and language knowledge encoded in pre-trained VLMs and language models (LLMs).
The key steps of the CDL framework are:

Extracting a preliminary list of generic visual concepts from image captions using dependency parsing and LLM-generated prompts.
Ranking and selecting the most visually discriminative concepts by measuring the mutual information between the VLM's visual recognition and the LLM's language-based concept relevance.
Fine-tuning the last layers of the pre-trained VLM to better align the concept activations with the selected concepts, in a self-supervised manner.

The authors then propose a suite of quantitative and human evaluation protocols to measure the interpretability, precision, thoroughness, and generalizability of the discovered concepts. Extensive experiments on six diverse visual recognition benchmarks demonstrate that the concepts discovered and learned by the CDL framework are indeed precise, thorough, and generalizable, providing strong evidence that pre-trained VLMs can effectively encode visual concepts.
The authors also show that the concepts discovered by CDL outperform previous state-of-the-art methods in both full-shot and few-shot visual recognition tasks, highlighting the practical benefits of the learned visual concepts.

Stats

Pre-trained vision-language models can learn visual concepts that are precise, thorough, and generalizable.
The discovered concepts outperform previous state-of-the-art methods in both full-shot and few-shot visual recognition tasks.
The CDL framework can discover category-agnostic and visually discriminative concepts from a large image-caption dataset.

Quotes

"Do vision-language models (VLMs) pre-trained to caption an image of a durian learn visual concepts such as brown (color) and spiky (texture) at the same time?"
"Visual concepts such as color, shape, and texture help models generalize compositionally, and can be incorporated into neuro-symbolic frameworks or offer concept-based explanations for classification decisions."
"We observe that recent works prompting VLMs with concepts often differ in their strategies to define and evaluate the visual concepts, leading to conflicting conclusions."

Key Insights Distilled From

Pre-trained Vision-Language Models Learn Discoverable Visual Concepts

by Yuan Zang,Ti... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12652.pdf

Deeper Inquiries

How can the discovered visual concepts be further leveraged to enable more transparent and interpretable multi-modal reasoning beyond just visual recognition?

The discovered visual concepts can be further leveraged to enable more transparent and interpretable multi-modal reasoning by incorporating them into neuro-symbolic frameworks. These visual concepts can serve as the building blocks for reasoning and understanding complex relationships between different modalities such as vision and language. By encoding these concepts into the reasoning process, the model can provide more interpretable explanations for its decisions and actions. For example, the visual concepts can be used to guide the model in generating explanations for its predictions, making the decision-making process more transparent and understandable to humans. Additionally, these concepts can be used to facilitate cross-modal reasoning, where the model can reason about objects or events using information from multiple modalities simultaneously. This can lead to more robust and accurate multi-modal reasoning capabilities, enabling the model to make more informed decisions based on a holistic understanding of the input data.

What are the limitations of the current pre-training approaches in VLMs that prevent them from learning certain types of visual concepts or their compositional relationships?

One limitation of current pre-training approaches in Vision-Language Models (VLMs) is the lack of explicit supervision for learning specific types of visual concepts or their compositional relationships. While VLMs are trained on large-scale datasets with contrastive learning objectives to align images and texts, they may not receive direct supervision on learning fine-grained visual attributes or complex compositional relationships between visual elements. This can result in VLMs struggling to capture nuanced visual concepts that require detailed understanding of object properties, relationships, or interactions. Additionally, the pre-training data may not cover the full spectrum of visual concepts present in the real world, leading to gaps in the model's knowledge and limiting its ability to generalize to unseen or complex scenarios. Furthermore, the pre-training objectives may prioritize certain types of information over others, potentially overlooking important visual concepts or relationships that are crucial for multi-modal reasoning tasks.

Can the proposed concept discovery and learning framework be extended to other modalities beyond vision, such as audio or tactile sensing, to enable cross-modal concept learning and reasoning?

Yes, the proposed concept discovery and learning framework can be extended to other modalities beyond vision, such as audio or tactile sensing, to enable cross-modal concept learning and reasoning. By adapting the framework to incorporate concepts from different modalities, the model can learn to associate visual concepts with auditory or tactile cues, allowing for a more comprehensive understanding of the environment. This cross-modal concept learning can enhance the model's ability to reason across different sensory inputs and make more informed decisions based on a holistic understanding of the multi-modal data. Additionally, the framework can be modified to accommodate the unique characteristics of audio or tactile data, enabling the model to extract relevant concepts and relationships from these modalities and integrate them into the reasoning process. This extension can lead to more robust and versatile multi-modal models capable of handling diverse types of sensory information and performing complex reasoning tasks across modalities.