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Found 5 papers, 3 papers with code
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence
Semantic correspondence have built up a new way for object recognition.
Synergistic saliency and depth prediction for RGB-D saliency detection
Evaluation on seven RGB-D datasets demonstrates that even without saliency ground truth for RGB-D datasets and using only the RGB data of RGB-D datasets at inference, our semi-supervised system performs favorable against state-of-the-art fully-supervised RGB-D saliency detection methods that use saliency ground truth for RGB-D datasets at training and depth data at inference on two largest testing datasets.
When Relation Networks meet GANs: Relation GANs with Triplet Loss
Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets.
Class-Conditional Domain Adaptation on Semantic Segmentation
We address this problem by introducing a Class-Conditional Domain Adaptation method (CCDA).
A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision
Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations.
