Semantically-Prompted Language Models Improve Visual Concept Descriptions

Core Concepts

Leveraging semantic knowledge bases and contrastive prompting, V-GLOSS generates detailed and distinguishing visual descriptions that improve performance on zero-shot vision tasks.

Abstract

The paper introduces V-GLOSS, a novel method for generating visual descriptions of concepts that leverages language models (LMs) and semantic knowledge bases (SKBs). The key ideas are:

Semantic Prompting: Conditioning the LM on structured semantic information from SKBs like WordNet to produce more expressive and specific descriptions.

Contrastive Prompting: Generating descriptions that highlight the distinguishing visual features between similar concepts, to address issues of class granularity.

The authors evaluate V-GLOSS on zero-shot image classification (ZSIC) and zero-shot class-conditional image generation (ZSCIG) tasks. Key findings:

V-GLOSS outperforms previous template-based and LM-based methods, achieving 1.8-2.6% higher accuracy on ImageNet, FGVC Aircraft, and Flowers 102 datasets in ZSIC.
V-GLOSS Silver, a dataset of ImageNet class descriptions generated by V-GLOSS, also improves performance on ZSIC and ZSCIG compared to using WordNet glosses.
The contrastive prompting variant is particularly effective at distinguishing visually similar classes, improving accuracy by 1.8% on average.
Semantic knowledge from SKBs and the synergy with LMs are crucial for generating high-quality visual descriptions that benefit downstream vision tasks.

Stats

"A tool with a spiral blade that is used to remove corks from bottles."
"A small brown bird with a black head and a white patch on its chest."
"A green vegetable with a thick stalk and florets that grow in a dense head."

Quotes

"High-quality visual descriptions are crucial in tasks such as zero-shot image classification and text-based image retrieval."
"By combining structured semantic information from SKBs such as WordNet and BabelNet, with a contrastive algorithm to finely distinguish similar classes, V-GLOSS is designed to mitigate the dual issues of granularity and ambiguity."
"Semantic similarity informs what classes we distinguish with our contrastive descriptions, and why they work."

Key Insights Distilled From

Semantically-Prompted Language Models Improve Visual Descriptions

by Michael Ogez... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2306.06077.pdf

Deeper Inquiries

How can V-GLOSS be extended to generate descriptions in multiple languages, beyond the current English-centric focus?

To extend V-GLOSS to generate descriptions in multiple languages, we can follow a few key steps:

Multilingual Semantic Knowledge Bases: Instead of relying solely on WordNet, we can incorporate multilingual semantic knowledge bases like BabelNet, which cover a wide range of languages. These resources provide a rich source of semantic information that can be leveraged by language models to generate descriptions in different languages.

Language-Specific Prompting: By providing language-specific prompts to the language model, we can guide it to generate descriptions in a particular language. This involves translating the class labels and descriptions into the target language before prompting the model.

Fine-Tuning on Multilingual Data: Fine-tuning the language model on multilingual datasets can help it learn to generate descriptions in different languages more effectively. By exposing the model to diverse linguistic contexts, it can improve its language generation capabilities across multiple languages.

Evaluation and Validation: It's crucial to evaluate the performance of the extended V-GLOSS on generating descriptions in multiple languages. This involves assessing the quality, accuracy, and fluency of the generated descriptions in different languages to ensure they are linguistically and culturally appropriate.

By incorporating these strategies, V-GLOSS can be extended to generate visual descriptions in multiple languages, catering to a more diverse and global audience.

What are the potential biases and limitations of using WordNet and other existing semantic knowledge bases to ground the visual descriptions?

Using WordNet and other semantic knowledge bases to ground visual descriptions can introduce biases and limitations:

Cultural and Linguistic Biases: WordNet may reflect biases inherent in the language and culture from which it was created. This can lead to skewed representations of concepts, especially when dealing with diverse or underrepresented groups.

Limited Coverage: Semantic knowledge bases may not cover all languages, domains, or cultural contexts equally. This can result in gaps in understanding and representation, particularly for niche or non-mainstream concepts.

Ambiguity and Polysemy: WordNet may struggle with handling ambiguous or polysemous words, leading to confusion in generating accurate descriptions. Different senses of a word may not be adequately distinguished, impacting the quality of the descriptions.

Outdated Information: Semantic knowledge bases may contain outdated or incorrect information, especially in rapidly evolving fields. This can affect the relevance and accuracy of the visual descriptions generated based on such data.

Lack of Contextual Understanding: Semantic knowledge bases may lack the ability to understand context or nuances in language, which can limit the depth and richness of the descriptions generated.

Over-reliance on Formalized Concepts: WordNet primarily focuses on formalized concepts and may not capture the full spectrum of informal or evolving language usage, leading to a restricted view of visual attributes.

Addressing these biases and limitations requires a critical evaluation of the semantic knowledge bases used, along with efforts to diversify and enhance the data sources and methodologies for grounding visual descriptions.

How can the synergy between language models and semantic knowledge be further leveraged to enable more general and flexible visual understanding capabilities?

To enhance the synergy between language models and semantic knowledge for improved visual understanding capabilities, several strategies can be implemented:

Contextual Embeddings: Incorporate contextual embeddings from language models to enrich the semantic information provided to the model. This can help capture nuanced relationships between concepts and improve the specificity of visual descriptions.

Dynamic Prompting: Develop dynamic prompting techniques that adapt the input based on the semantic context. By adjusting the prompts to focus on relevant semantic features, the model can generate more accurate and contextually appropriate visual descriptions.

Cross-Modal Learning: Implement cross-modal learning approaches that leverage both textual and visual data to enhance the understanding of concepts. By training the model on multimodal data, it can learn to associate textual descriptions with visual attributes more effectively.

Fine-Grained Discrimination: Utilize semantic knowledge to enable fine-grained discrimination between visually similar concepts. By providing detailed semantic information, the model can distinguish subtle differences and generate more precise visual descriptions.

Multilingual Capabilities: Extend the model's multilingual capabilities by incorporating diverse semantic knowledge bases in different languages. This can broaden the scope of visual understanding across various linguistic contexts and cultural backgrounds.

Bias Mitigation Techniques: Implement bias mitigation techniques to address potential biases in the semantic knowledge bases and language models. By actively identifying and mitigating biases, the model can generate more inclusive and unbiased visual descriptions.

By leveraging these strategies, the synergy between language models and semantic knowledge can be optimized to enhance the generalizability, flexibility, and accuracy of visual understanding capabilities.