30 citations · 37 across the 14 of their papers we have counts for
5 papers · 1 filter
SPELUNKA: A Framework for Finding Unanticipated Associations in Vision-Language Models
Cassandra Parent, Isha Agarwal, Kumail Alhamoud +4
Vision-Language Models (VLMs) are the backbone of many state-of-the-art multimodal systems, but often encode spurious and harmful associations from their training data. While prior…
SpaceVLM: Sub-Space Modeling of Negation in Vision-Language Models
Sepehr Kazemi Ranjbar, Kumail Alhamoud, Marzyeh Ghassemi
Vision-Language Models (VLMs) struggle with negation. Given a prompt like "retrieve (or generate) a street scene without pedestrians," they often fail to respect the "not." Existin…
Vision-Language Models Do Not Understand Negation
Kumail Alhamoud, Shaden Alshammari, Yonglong Tian +4
Many practical vision-language applications require models that understand negation, e.g., when using natural language to retrieve images which contain certain objects but not othe…
PIVOT: Prompting for Video Continual Learning
Andrés Villa, Juan León Alcázar, Motasem Alfarra +5
Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult…
vCLIMB: A Novel Video Class Incremental Learning Benchmark
Andrés Villa, Kumail Alhamoud, Juan León Alcázar +3
Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in uns…