8 citations · 11 across the 3 of their papers we have counts for
5 papers
Language Models as Black-Box Optimizers for Vision-Language Models
Shihong Liu, Zhiqiu Lin, Samuel Yu +4
Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs…
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models
Zhiqiu Lin, Samuel Yu, Zhiyi Kuang +2
The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of…
PACS: A Dataset for Physical Audiovisual CommonSense Reasoning
Samuel Yu, Peter Wu, Paul Pu Liang +2
In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamenta…
Street Crossing Aid Using Light-weight CNNs for the Visually Impaired
Samuel Yu, Heon Lee, Jung Hoon Kim
In this paper, we address an issue that the visually impaired commonly face while crossing intersections and propose a solution that takes form as a mobile application. The applica…
LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired
Samuel Yu, Heon Lee, John Kim
Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes…