Publications (43)
Frequency Is What You Need: Considering Word Frequency When Text Masking Benefits Vision-Language Model Pre-training
Mingliang Liang, Martha Larson
Vision Language Models (VLMs) can be trained more efficiently if training sets can be reduced in size. Recent work has shown the benefits of masking text during VLM training using…
Factorization Machines for Data with Implicit Feedback
Babak Loni, Martha Larson, Alan Hanjalic
In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback. The optimization…
Enhancing Vision-Language Model Pre-training with Image-text Pair Pruning Based on Word Frequency
Mingliang Liang, Martha Larson
We propose Word-Frequency-based Image-Text Pair Pruning (WFPP), a novel data pruning method that improves the efficiency of VLMs. Unlike MetaCLIP, our method does not need metadata…
The Importance of Image Interpretation: Patterns of Semantic Misclassification in Real-World Adversarial Images
Zhengyu Zhao, Nga Dang, Martha Larson
Adversarial images are created with the intention of causing an image classifier to produce a misclassification. In this paper, we propose that adversarial images should be evaluat…
Talking to Extraordinary Objects: Folktales Offer Analogies for Interacting with Technology
Martha Larson
Speech and language are valuable for interacting with technology. It would be ideal to be able to decouple their use from anthropomorphization, which has recently met an important…
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Jeroen B. P. Vuurens, Martha Larson, Arjen P. de Vries
Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new…