9 citations · 18 across the 13 of their papers we have counts for
13 papers
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
Yiran Xu, Siqi Xie, Zhuofang Li +13
Image retargeting aims to change the aspect-ratio of an image while maintaining its content and structure with less visual artifacts. Existing methods still generate many artifacts…
Calibrated Multi-Preference Optimization for Aligning Diffusion Models
Kyungmin Lee, Xiaohang Li, Qifei Wang +7
Aligning text-to-image (T2I) diffusion models with preference optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability…
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling
William Yicheng Zhu, Keren Ye, Junjie Ke +4
Recognizing and disentangling visual attributes from objects is a foundation to many computer vision applications. While large vision language representations like CLIP had largely…
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
Seung Hyun Lee, Jijun Jiang, Yiran Xu +10
The goal of image cropping is to identify visually appealing crops in an image. Conventional methods are trained on specific datasets and fail to adapt to new requirements. Recent…
Optical Diffusion Models for Image Generation
Ilker Oguz, Niyazi Ulas Dinc, Mustafa Yildirim +6
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained n…
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
Seung Hyun Lee, Yinxiao Li, Junjie Ke +11
Recent works have demonstrated that using reinforcement learning (RL) with multiple quality rewards can improve the quality of generated images in text-to-image (T2I) generation. H…
Rich Human Feedback for Text-to-Image Generation
Youwei Liang, Junfeng He, Gang Li +15
Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. How…
Forward-Forward Training of an Optical Neural Network
Ilker Oguz, Junjie Ke, Qifei Wang +6
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementa…
MRET: Multi-resolution Transformer for Video Quality Assessment
Junjie Ke, Tianhao Zhang, Yilin Wang +2
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA ta…
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Junjie Ke, Keren Ye, Jiahui Yu +3
Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic…
MUSIQ: Multi-scale Image Quality Transformer
Junjie Ke, Qifei Wang, Yilin Wang +2
Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional…
Adversarially Adaptive Normalization for Single Domain Generalization
Xinjie Fan, Qifei Wang, Junjie Ke +3
Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversaria…
Multi-path Neural Networks for On-device Multi-domain Visual Classification
Qifei Wang, Junjie Ke, Joshua Greaves +9
Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constra…