4 citations · 5 across the 5 of their papers we have counts for
5 papers
To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods typically follow either background…
FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignment with user-provided layouts when…
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
Chenxi Xie, Changqun Xia, Mingcan Ma +3
Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution in…
Receptive Field Broadening and Boosting for Salient Object Detection
Mingcan Ma, Changqun Xia, Chenxi Xie +2
Salient object detection requires a comprehensive and scalable receptive field to locate the visually significant objects in the image. Recently, the emergence of visual transforme…
Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss
Jia Li, Jinming Su, Changqun Xia +2
By the aid of attention mechanisms to weight the image features adaptively, recent advanced deep learning-based models encourage the predicted results to approximate the ground-tru…