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Found 3 papers, 3 papers with code
Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
Specifically, it achieves $43. 6\%$ mIoU on ADE20K with only $4. 0$ GFLOPs, which is $0. 9\%$ and $2. 5\%$ mIoU better than SeaFormer and SegNeXt but with about $38. 0\%$ fewer GFLOPs.
Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Specifically, we employ the coarse masks obtained from the fixed prototypes as a guide to adjust the fixed prototype towards the center of the semantic and spatial domains in the test image.
Dual Relation Knowledge Distillation for Object Detection
By distilling the global pixel relation, the student detector can learn the relation between foreground and background features, and avoid the difficulty of distilling features directly for the feature imbalance issue.
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