1 citations · 1 across the 7 of their papers we have counts for
7 papers
Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression
Qi Mao, Zijian Wang, Zhengxue Cheng +2
Learned image compression (LIC) increasingly requires reconstructions that balance distortion fidelity and perceptual realism across a wide range of bitrates. However, most existin…
Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer
Naifu Xue, Qi Mao, Zijian Wang +2
Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data. However, these advancements primarily focus on producing…
Extreme Image Compression using Fine-tuned VQGANs
Qi Mao, Tinghan Yang, Yinuo Zhang +4
Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bit…
ShowFace: Coordinated Face Inpainting with Memory-Disentangled Refinement Networks
Zhuojie Wu, Xingqun Qi, Zijian Wang +4
Face inpainting aims to complete the corrupted regions of the face images, which requires coordination between the completed areas and the non-corrupted areas. Recently, memory-ori…
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning
Xingqun Qi, Muyi Sun, Zijian Wang +5
Biphasic face photo-sketch synthesis has significant practical value in wide-ranging fields such as digital entertainment and law enforcement. Previous approaches directly generate…
Self-supervised Correlation Mining Network for Person Image Generation
Zijian Wang, Xingqun Qi, Kun Yuan +1
Person image generation aims to perform non-rigid deformation on source images, which generally requires unaligned data pairs for training. Recently, self-supervised methods expres…
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation
Wenxuan Zou, Muyi Sun
In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma…