20 citations · 32 across the 4 of their papers we have counts for
4 papers
PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting
Zixin Guo, Tzu-Jui Julius Wang, Selen Pehlivan +2
Vision-language (VL) Pre-training (VLP) has shown to well generalize VL models over a wide range of VL downstream tasks, especially for cross-modal retrieval. However, it hinges on…
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Tzu-Jui Julius Wang, Jorma Laaksonen, Tomas Langer +2
Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP…
CLIP4IDC: CLIP for Image Difference Captioning
Zixin Guo, Tzu-Jui Julius Wang, Jorma Laaksonen
Image Difference Captioning (IDC) aims at generating sentences to describe differences between two similar-looking images. Conventional approaches learn an IDC model with a pre-tra…
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models
Tzu-Jui Julius Wang, Selen Pehlivan, Jorma Laaksonen
Predicting a scene graph that captures visual entities and their interactions in an image has been considered a crucial step towards full scene comprehension. Recent scene graph ge…