Search Results - Proceedings - IEEE Computer Society Conference on Pattern Recognition AND Image Processing*

1st Canadian Conference on Computer and Robot Vision : University of Western Ontario, London, Ontario, Canada, May 17-19, 2004 : proceedings by Canadian Conference on Computer and Robot Vision, Canadian Image Processing and Pattern Recognition Society, International Association for Pattern Recognition, IEEE Computer Society

Get full text

Proceedings : 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 18-20, 1996, San Francisco, California by IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society. Technical Committee on Machine Intelligence and Pattern Analysis

Get full text

AdversarialNAS: Adversarial Neural Architecture Search for GANs by Gao, Chen, Chen, Yunpeng, Liu, Si, Tan, Zhenxiong, Yan, Shuicheng

“…) to search for a superior generative model on the task of unconditional image generation…”
Get full text

Pre-Trained Image Processing Transformer by Chen, Hanting, Wang, Yunhe, Guo, Tianyu, Xu, Chang, Deng, Yiping, Liu, Zhenhua, Ma, Siwei, Xu, Chunjing, Xu, Chao, Gao, Wen

“…) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs…”
Get full text

MAXIM: Multi-Axis MLP for Image Processing by Tu, Zhengzhong, Talebi, Hossein, Zhang, Han, Yang, Feng, Milanfar, Peyman, Bovik, Alan, Li, Yinxiao

“… Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for lowlevel vision…”
Get full text

Image Processing Using Multi-Code GAN Prior by Gu, Jinjin, Shen, Yujun, Zhou, Bolei

“…Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging…”
Get full text

Invertible Image Signal Processing by Xing, Yazhou, Qian, Zian, Chen, Qifeng

“…Unprocessed RAW data is a highly valuable image format for image editing and computer vision…”
Get full text

PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling by Yan, Xu, Zheng, Chaoda, Li, Zhen, Wang, Sheng, Cui, Shuguang

“… In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively…”
Get full text

Unifying Vision, Text, and Layout for Universal Document Processing by Tang, Zineng, Yang, Ziyi, Wang, Guoxin, Fang, Yuwei, Liu, Yang, Zhu, Chenguang, Zeng, Michael, Zhang, Cha, Bansal, Mohit

“…We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation…”
Get full text

Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art by Fuhl, Wolfgang, Tonsen, Marc, Bulling, Andreas, Kasneci, Enkelejda

“… & applications, ACM. New York, NY, USA, pp 123–130, 2016 ), ExCuSe (Fuhl et al. in Computer analysis of images and patterns…”
Get full text

High-Resolution Image Synthesis with Latent Diffusion Models by Rombach, Robin, Blattmann, Andreas, Lorenz, Dominik, Esser, Patrick, Ommer, Bjorn

“…By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs…”
Get full text

Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines by Mosleh, Ali, Sharma, Avinash, Onzon, Emmanuel, Mannan, Fahim, Robidoux, Nicolas, Heide, Felix

“…Commodity imaging systems rely on hardware image signal processing (ISP) pipelines. These low-level pipelines consist of a sequence of processing blocks…”
Get full text

Null-text Inversion for Editing Real Images using Guided Diffusion Models by Mokady, Ron, Hertz, Amir, Aberman, Kfir, Pritch, Yael, Cohen-Or, Daniel

“…Recent large-scale text-guided diffusion models provide powerful image generation capabilities…”
Get full text

Vector Quantized Diffusion Model for Text-to-Image Synthesis by Gu, Shuyang, Chen, Dong, Bao, Jianmin, Wen, Fang, Zhang, Bo, Chen, Dongdong, Yuan, Lu, Guo, Baining

“…We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation…”
Get full text

BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models by Li, Bo, Xue, Kaitao, Liu, Bin, Lai, Yu-Kun

“…Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models (DM…”
Get full text

Images Speak in Images: A Generalist Painter for In-Context Visual Learning by Wang, Xinlong, Wang, Wen, Cao, Yue, Shen, Chunhua, Huang, Tiejun

“… But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general-purpose task prompts…”
Get full text

DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation by Kim, Gwanghyun, Kwon, Taesung, Ye, Jong Chul

“…Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP…”
Get full text

SINE: SINgle Image Editing with Text-to-Image Diffusion Models by Zhang, Zhixing, Han, Ligong, Ghosh, Arnab, Metaxas, Dimitris, Ren, Jian

“…Recent works on diffusion models have demonstrated a strong capability for conditioning image generation, e.g…”
Get full text

CNN-Generated Images Are Surprisingly Easy to Spot... for Now by Wang, Sheng-Yu, Wang, Oliver, Zhang, Richard, Owens, Andrew, Efros, Alexei A.

“…). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN…”
Get full text

Hyperbolic Image Embeddings by Khrulkov, Valentin, Mirvakhabova, Leyla, Ustinova, Evgeniya, Oseledets, Ivan, Lempitsky, Victor

“…Computer vision tasks such as image classification, image retrieval, and few-shot learning are currently dominated by Euclidean and spherical embeddings so that the final decisions about class…”
Get full text