Papers by Garima Pruthi

2 papers
KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models (2023.acl-industry)

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Challenge: Image ad understanding is a crucial task with wide real-world applications, but is under-explored in the machine learning community due to the lack of foundational vision-language models (VLMs) .
Approach: They propose a simple feature adaptation strategy to fuse multimodal information for image ads and further empower it with knowledge of real-world entities.
Outcome: The proposed strategy fuses multimodal information for image ads and empowers it with knowledge of real-world entities.
PRISM: A New Lens for Improved Color Understanding (2024.emnlp-industry)

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Challenge: PRISM is a visual representation learner that can grasp the nuances of precise colors without compromising CLIP’s performance on established benchmarks.
Approach: They propose a method that extends CLIP's ability to grasp the nuances of precise colors by utilizing a curated dataset of 100 image-text pairs that can be effortlessly repurposed for fine-tuning.
Outcome: The proposed method improves CLIP's ability to grasp the nuances of precise colors without compromising CLIP’s performance on established benchmarks.

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