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Found 2 papers, 1 papers with code
Task-oriented Memory-efficient Pruning-Adapter
Adapters are to freeze the model and give it a new weight matrix on the side, which can significantly reduce the time and memory of training, but the cost is that the evaluation and testing will increase the time and memory consumption.
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
For the Depth branch, we propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
