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Found 5 papers, 3 papers with code
Attention Weighted Local Descriptors
Local features detection and description are widely used in many vision applications with high industrial and commercial demands.
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
In addition, to further improve the segmentation accuracy, we design a Variation-aware Refine Module to enhance the local consistency of pseudo-labels by computing pixel-level variation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation
To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap.
MTLDesc: Looking Wider to Describe Better
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results.
