no code implementations • 18 Dec 2023 • Chenyang Qi, Zhengzhong Tu, Keren Ye, Mauricio Delbracio, Peyman Milanfar, Qifeng Chen, Hossein Talebi
Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement.
1 code implementation • CVPR 2023 • Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang
Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines.
Ranked #46 on Video Quality Assessment on MSU SR-QA Dataset
no code implementations • 12 May 2022 • Keren Ye, Adriana Kovashka
We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries.
1 code implementation • CVPR 2021 • Keren Ye, Adriana Kovashka
Prior work in scene graph generation requires categorical supervision at the level of triplets - subjects and objects, and predicates that relate them, either with or without bounding box information.
no code implementations • 4 Jan 2021 • Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew Howard, Marco Fornoni
In this paper we address the question: can task-specific detectors be trained and represented as a shared set of weights, plus a very small set of additional weights for each task?
1 code implementation • ICCV 2019 • Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent
Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision.
no code implementations • 25 Nov 2018 • Keren Ye, Mingda Zhang, Wei Li, Danfeng Qin, Adriana Kovashka, Jesse Berent
To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels.
no code implementations • 29 Jul 2018 • Keren Ye, Kyle Buettner, Adriana Kovashka
We dedicate our study to understand the dynamic structure of video ads automatically.
no code implementations • ECCV 2018 • Keren Ye, Adriana Kovashka
In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism.
no code implementations • CVPR 2017 • Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, Adriana Kovashka
There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action.