Vision-Language Models: Efficiency, Inclusivity, and Bias Mitigation

The recent advancements in Vision-Language Models (VLM) have significantly pushed the boundaries of image classification and retrieval, particularly in low-resource and few-class domains. Innovations in retrieval-based strategies and the integration of dense neural networks with efficient indexing systems have shown remarkable improvements in classification accuracy and retrieval speed. Notably, the field is witnessing a shift towards more inclusive benchmarks that incorporate diverse languages and cultural perspectives, addressing the need for multilinguality in vision-language tasks. Additionally, there is a growing emphasis on mitigating biases within VLMs, with novel approaches focusing on fine-grained debiasing techniques that adapt to individual inputs rather than applying a uniform correction. These developments collectively indicate a trend towards more robust, efficient, and culturally sensitive VLM applications, with a particular focus on enhancing performance in niche and underrepresented domains.

Sources

Retrieval-enriched zero-shot image classification in low-resource domains

Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement

Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification

No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages

RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models

BendVLM: Test-Time Debiasing of Vision-Language Embeddings

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