2 citations · 2 across the 2 of their papers we have counts for
12 papers · 1 filter
Effectively Leveraging Attributes for Visual Similarity
Samarth Mishra, Zhongping Zhang, Yuan Shen +3
Measuring similarity between two images often requires performing complex reasoning along different axes (e.g., color, texture, or shape). Insights into what might be important for…
Learning to Scale Multilingual Representations for Vision-Language Tasks
Andrea Burns, Donghyun Kim, Derry Wijaya +2
Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are a…
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh +3
Existing unsupervised domain adaptation methods aim to transfer knowledge from a label-rich source domain to an unlabeled target domain. However, obtaining labels for some source d…
LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval
Reuben Tan, Huijuan Xu, Kate Saenko +1
The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations durin…
MULE: Multimodal Universal Language Embedding
Donghyun Kim, Kuniaki Saito, Kate Saenko +2
Existing vision-language methods typically support two languages at a time at most. In this paper, we present a modular approach which can easily be incorporated into existing visi…
Learning Similarity Conditions Without Explicit Supervision
Reuben Tan, Mariya I. Vasileva, Kate Saenko +1
Many real-world tasks require models to compare images along multiple similarity conditions (e.g. similarity in color, category or shape). Existing methods often reason about these…