17 citations · 21 across the 3 of their papers we have counts for
3 papers
cs.CL2023
Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion
Jie S. Li, Yow-Ting Shiue, Yong-Siang Shih +1
This paper describes our zero-shot approaches for the Visual Word Sense Disambiguation (VWSD) Task in English. Our preliminary study shows that the simple approach of matching cand…
cs.CV2021★ 4 cited
Semi-supervised Contrastive Learning with Similarity Co-calibration
Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3
Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…
cs.LG2019★ 17 cited
AutoML using Metadata Language Embeddings
Iddo Drori, Lu Liu, Yi Nian +5
As a human choosing a supervised learning algorithm, it is natural to begin by reading a text description of the dataset and documentation for the algorithms you might use. We demo…