Conditioned Image Retrieval for Fashion using Contrastive Learning and CLIP-based Features

Author:

Baldrati Alberto1,Bertini Marco1,Uricchio Tiberio1,Del Bimbo Alberto1

Affiliation:

1. University of Florence, Italy

Funder

NVIDIA

European Horizon 2020 Programme

Publisher

ACM

Reference31 articles.

1. Sandhini Agarwal Gretchen Krueger Jack Clark Alec Radford Jong Wook Kim and Miles Brundage. 2021. Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications. arXiv preprint arXiv:2108.02818(2021). Sandhini Agarwal Gretchen Krueger Jack Clark Alec Radford Jong Wook Kim and Miles Brundage. 2021. Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications. arXiv preprint arXiv:2108.02818(2021).

2. Object-oriented convolutional features for fine-grained image retrieval in large surveillance datasets

3. Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs

4. Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165(2020). Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165(2020).

5. Yanbei Chen and Loris Bazzani. 2020. Learning Joint Visual Semantic Matching Embeddings for Language-Guided Retrieval. 136-152. https://doi.org/10.1007/978-3-030-58542-6_9 10.1007/978-3-030-58542-6_9

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