56 citations · 257 across the 54 of their papers we have counts for
5 papers · 1 filter
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…
Language Features Matter: Effective Language Representations for Vision-Language Tasks
Andrea Burns, Reuben Tan, Kate Saenko +2
Shouldn't language and vision features be treated equally in vision-language (VL) tasks? Many VL approaches treat the language component as an afterthought, using simple language m…
Why do These Match? Explaining the Behavior of Image Similarity Models
Bryan A. Plummer, Mariya I. Vasileva, Vitali Petsiuk +2
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models fo…