Search Results for author: Mariya Hendriksen

Found 8 papers, 5 papers with code

Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning

1 code implementation27 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.

Contrastive Learning Representation Learning

Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control

1 code implementation27 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.

Image Retrieval Retrieval +1

Multimodal Learned Sparse Retrieval for Image Suggestion

no code implementations12 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).

Image Captioning Retrieval +1

Towards Contrastive Learning in Music Video Domain

no code implementations1 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.

Contrastive Learning Genre classification +3

Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study

1 code implementation12 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.

Cross-Modal Retrieval Object +1

Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

no code implementations16 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.

Descriptive

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