1 code implementation • 27 Feb 2024 • Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke
Hence, contrastive losses are not sufficient to learn task-optimal representations, i. e., representations that contain all task-relevant information shared between the image and associated captions.
1 code implementation • 27 Feb 2024 • Thong Nguyen, Mariya Hendriksen, Andrew Yates, Maarten de Rijke
Our proposed approach efficiently transforms dense vectors from a frozen dense model into sparse lexical vectors.
no code implementations • 12 Feb 2024 • Thong Nguyen, Mariya Hendriksen, Andrew Yates
Motivated by this, in this work, we explore the application of LSR in the multi-modal domain, i. e., we focus on Multi-Modal Learned Sparse Retrieval (MLSR).
no code implementations • 1 Sep 2023 • Karel Veldkamp, Mariya Hendriksen, Zoltán Szlávik, Alexander Keijser
To gain a better understanding of the reasons contrastive learning was not successful for music videos, we perform a qualitative analysis of the learned representations, revealing why contrastive learning might have difficulties uniting embeddings from two modalities.
1 code implementation • 12 Jan 2023 • Mariya Hendriksen, Svitlana Vakulenko, Ernst Kuiper, Maarten de Rijke
Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets.
1 code implementation • SIGIR 2022 Workshop on eCommerce 2022 • Mariya Hendriksen, Viggo Overes
The popularity of online fashion shopping continues to grow.
1 code implementation • 21 Dec 2021 • Mariya Hendriksen, Maurits Bleeker, Svitlana Vakulenko, Nanne van Noord, Ernst Kuiper, Maarten de Rijke
One aspect of this data is a category tree that is being used in search and recommendation.
no code implementations • 16 Dec 2020 • Mariya Hendriksen, Ernst Kuiper, Pim Nauts, Sebastian Schelter, Maarten de Rijke
In this paper, we focus on purchase prediction for both anonymous and identified sessions on an e-commerce platform.