Enhancing Object Recognition Across Diverse Geographies through Descriptive Knowledge Prompting

Core Concepts

Incorporating descriptive knowledge of objects across different geographical regions can enhance the robustness of object recognition models to geographical domain shifts.

Abstract

The content explores strategies to improve the geographical robustness of object recognition models, which often degrade in performance when tested in new geographies due to shifts in object design, materials, and context. The key highlights are: Probing CLIP's internal knowledge by including country names in prompts can improve recognition, especially in Africa and Asia, as it aligns representations to these regions. Gathering descriptive knowledge of objects from an external large language model (LLM) for different countries can further boost performance over CLIP's default prompts, suggesting CLIP's internal knowledge may be incomplete. Combining CLIP's internal country knowledge and the LLM's descriptive knowledge provides the best zero-shot performance, indicating the complementary nature of these sources. To address overfitting of soft prompts to a limited source geography (e.g. Europe) during training, the authors propose a geography knowledge regularization technique. This ensures the learned class representations generalize better to unseen target geographies. The regularized soft prompts outperform few-shot target-trained prompts, showing the effectiveness of the proposed approach in the absence of target data. The method provides larger gains on classes that are most difficult for the baseline soft prompting method, indicating its ability to address geographical biases in object representations. Overall, the work demonstrates the importance of incorporating descriptive geographical knowledge to enhance the geographical robustness of object recognition models.

Stats

The GDP per capita and Human Development Index of a country have the strongest correlation with the distance between CLIP class embeddings across countries. The average yearly temperature and precipitation also show moderate correlations, suggesting a potential role of climate in object differences across geographies.

Quotes

"Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context." "Fortunately, geographical shifts have a unique property compared to other common domain shifts (e.g. ones due to artistic style or weather changes)—they can be addressed with descriptive knowledge about concept changes."

Key Insights Distilled From

Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition

by Kyle Buettne... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2401.01482.pdf

Deeper Inquiries

How can we ensure the descriptive knowledge obtained from large language models is fully representative and unbiased across all regions and socioeconomic levels

To ensure that the descriptive knowledge obtained from large language models is fully representative and unbiased across all regions and socioeconomic levels, several strategies can be implemented: Diverse Training Data: Incorporating a wide range of training data from various regions and socioeconomic backgrounds can help in capturing a more comprehensive understanding of object representations. This data should be carefully curated to include diverse perspectives and avoid biases. Validation and Verification: It is essential to validate the descriptive knowledge obtained from large language models by cross-referencing it with reliable sources and experts in the field. This can help in identifying and correcting any inaccuracies or biases in the knowledge. Continuous Monitoring: Regularly monitoring and updating the descriptive knowledge to reflect changes in societal norms, cultural practices, and economic conditions can ensure its relevance and accuracy over time. Ethical Considerations: Ethical guidelines and frameworks should be established to guide the collection and utilization of descriptive knowledge, ensuring fairness, transparency, and accountability in the process. Collaboration and Feedback: Engaging with diverse communities and stakeholders to gather feedback and insights on the descriptive knowledge can help in refining and improving its representativeness and inclusivity. By implementing these measures, we can enhance the quality and fairness of the descriptive knowledge obtained from large language models, making it more reflective of the diverse global landscape.

What other types of external knowledge, beyond textual descriptions, could be leveraged to further improve geographical robustness in object recognition

In addition to textual descriptions, several other types of external knowledge can be leveraged to further improve geographical robustness in object recognition: Visual Data: Incorporating visual data such as satellite imagery, street view images, or cultural artifacts can provide additional context and visual cues that enhance the understanding of object representations in different geographical settings. Geospatial Information: Utilizing geospatial data, including maps, terrain information, climate data, and land use patterns, can help in contextualizing object recognition tasks within specific geographic regions. Cultural Knowledge: Leveraging cultural knowledge, including traditions, customs, and societal practices unique to different regions, can aid in capturing the nuances and variations in object representations influenced by cultural factors. Historical Context: Considering historical information about regions, including architectural styles, urban development, and historical events, can provide valuable insights into the evolution of object representations over time. Economic Indicators: Integrating economic indicators such as GDP per capita, income levels, and industry profiles can help in understanding the material choices, design preferences, and usage patterns of objects in different socioeconomic contexts. By incorporating a diverse range of external knowledge sources, object recognition models can achieve greater geographical robustness and cultural sensitivity in their representations.

How can the insights from this work be extended to other computer vision tasks beyond object recognition, such as scene understanding or activity recognition, to achieve more equitable AI systems across the globe

The insights from this work can be extended to other computer vision tasks beyond object recognition to promote more equitable AI systems across the globe: Scene Understanding: By incorporating geographical knowledge and descriptive cues into scene understanding tasks, AI systems can better interpret and analyze complex scenes in diverse environments. This can lead to more accurate scene segmentation, object localization, and contextual understanding. Activity Recognition: Integrating geographical context and cultural insights into activity recognition models can enhance their ability to recognize and interpret human actions in different settings. This can improve the performance of AI systems in understanding diverse activities and behaviors across various regions. Cross-Domain Applications: The principles of incorporating external knowledge and regularizing model training for geographical robustness can be applied to various computer vision tasks, such as image captioning, visual question answering, and image generation. By considering geographical and cultural factors, AI systems can generate more contextually relevant and culturally sensitive outputs. By extending these insights to a broader range of computer vision tasks, we can advance the development of AI systems that are more inclusive, diverse, and globally aware.