181 citations · 1.7k across the 107 of their papers we have counts for
10 papers · 1 filter
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Miaoge Li, Dongsheng Wang, Xinyang Liu +4
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent spac…
Class-Balancing Diffusion Models
Yiming Qin, Huangjie Zheng, Jiangchao Yao +2
Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified…
In-Context Learning Unlocked for Diffusion Models
Zhendong Wang, Yifan Jiang, Yadong Lu +5
We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to…
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Zhendong Wang, Yifan Jiang, Huangjie Zheng +5
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the tr…
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
Xinyang Liu, Dongsheng Wang, Bowei Fang +5
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, whic…
Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks with a simple architecture and relatively small computational cost. The…
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…
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning
Dandan Guo, Ruiying Lu, Bo Chen +2
Observing a set of images and their corresponding paragraph-captions, a challenging task is to learn how to produce a semantically coherent paragraph to describe the visual content…
Self-supervised Pre-training with Hard Examples Improves Visual Representations
Chunyuan Li, Xiujun Li, Lei Zhang +3
Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling fra…
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
Hao Zhang, Bo Chen, Long Tian +2
For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that int…