42 citations · 244 across the 31 of their papers we have counts for
37 papers
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork
Xin Yuan, Zhe Lin, Jason Kuen +2
We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives. U…
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Haitian Zheng, Zhe Lin, Jingwan Lu +7
Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partiall…
Lite Vision Transformer with Enhanced Self-Attention
Chenglin Yang, Yilin Wang, Jianming Zhang +4
Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predi…
SSH: A Self-Supervised Framework for Image Harmonization
Yifan Jiang, He Zhang, Jianming Zhang +7
Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background image…
Single-image Full-body Human Relighting
Manuel Lagunas, Xin Sun, Jimei Yang +5
We present a single-image data-driven method to automatically relight images with full-body humans in them. Our framework is based on a realistic scene decomposition leveraging pre…
Multimodal Contrastive Training for Visual Representation Learning
Xin Yuan, Zhe Lin, Jason Kuen +5
We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlik…
Learning to Recover 3D Scene Shape from a Single Image
Wei Yin, Jianming Zhang, Oliver Wang +4
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth sh…
Meticulous Object Segmentation
Chenglin Yang, Yilin Wang, Jianming Zhang +3
Compared with common image segmentation tasks targeted at low-resolution images, higher resolution detailed image segmentation receives much less attention. In this paper, we propo…
Semantic Layout Manipulation with High-Resolution Sparse Attention
Haitian Zheng, Zhe Lin, Jingwan Lu +4
We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transf…
Mask Guided Matting via Progressive Refinement Network
Qihang Yu, Jianming Zhang, He Zhang +5
We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matti…
Deep Image Compositing
He Zhang, Jianming Zhang, Federico Perazzi +2
Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high qualit…
Attribute-conditioned Layout GAN for Automatic Graphic Design
Jianan Li, Jimei Yang, Jianming Zhang +3
Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (…
Adversarial Knowledge Transfer from Unlabeled Data
Akash Gupta, Rameswar Panda, Sujoy Paul +2
While machine learning approaches to visual recognition offer great promise, most of the existing methods rely heavily on the availability of large quantities of labeled training d…
Shape Adaptor: A Learnable Resizing Module
Shikun Liu, Zhe Lin, Yilin Wang +3
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and st…
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Xihui Liu, Zhe Lin, Jianming Zhang +4
We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions. It is a challenging task considering…
SSN: Soft Shadow Network for Image Compositing
Yichen Sheng, Jianming Zhang, Bedrich Benes
We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to imag…
High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Yu Zeng, Zhe Lin, Jimei Yang +3
Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. To address this challenge, we propose an iterative inpainting method w…
Explainable Deep Classification Models for Domain Generalization
Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi +5
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object…
LeRoP: A Learning-Based Modular Robot Photography Framework
Hao Kang, Jianming Zhang, Haoxiang Li +3
We introduce a novel framework for automatic capturing of human portraits. The framework allows the robot to follow a person to the desired location using a Person Re-identificatio…
Scaling Object Detection by Transferring Classification Weights
Jason Kuen, Federico Perazzi, Zhe Lin +2
Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number of object-level categories annot…
Towards High-Resolution Salient Object Detection
Yi Zeng, Pingping Zhang, Jianming Zhang +2
Deep neural network based methods have made a significant breakthrough in salient object detection. However, they are typically limited to input images with low resolutions ($400\t…
Multi-way Encoding for Robustness
Donghyun Kim, Sarah Adel Bargal, Jianming Zhang +1
Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to a…
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning
Peng Zhou, Long Mai, Jianming Zhang +3
Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performan…
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Jianan Li, Jimei Yang, Aaron Hertzmann +2
Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric rel…