70 citations · 162 across the 15 of their papers we have counts for
15 papers
KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models
Zhiwei Jia, Pradyumna Narayana, Arjun R. Akula +4
Image ad understanding is a crucial task with wide real-world applications. Although highly challenging with the involvement of diverse atypical scenes, real-world entities, and re…
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
Weixi Feng, Wanrong Zhu, Tsu-jui Fu +6
Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on…
Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
Xuehai He, Weixi Feng, Tsu-Jui Fu +6
Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual c…
MetaCLUE: Towards Comprehensive Visual Metaphors Research
Arjun R. Akula, Brendan Driscoll, Pradyumna Narayana +9
Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creativ…
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu +6
Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we o…
CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng +7
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP. However, existing prompt t…