Semantic sementation

nanonets.com
https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef solves a Udacity course problem.

https://nanonets.com/ learn your drone to count the number of solar panels.

https://github.com/CSAILVision/semantic-segmentation-pytorch of http://sceneparsing.csail.mit.edu/

https://github.com/NVlabs/SPADE Semantic Image Synthesis with Spatially-Adaptive Normalization. We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is suboptimal as the normalization layers tend to ``wash away'' semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows user control over both semantic and style as synthesizing images. by https://github.com/junyanz

parts
https://github.com/NVlabs/SCOPS

https://varunjampani.github.io/scops/ Parts provide a intermediate representation of objects that is robust with respect to the camera, pose and appearance variations. Existing works on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and can not generalize to unseen object categories. We propose a self-supervised deep learning approach for part segmentation, where we devise several loss functions that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Extensive experiments on different types of image collections demonstrate that our approach can produce part segments that adhere to object boundaries and also more semantically consistent across object instances compared to existing self-supervised techniques.

https://varunjampani.github.io/codes semantic segmentation repos as released by nvidia labs.

pythia
https://github.com/facebookresearch/pythia Pythia is a modular framework for supercharging vision and language research built on top of PyTorch.

pac
For super image resolution upscaling. https://github.com/Yijunmaverick/DeepJointFilter

https://suhangpro.github.io/pac/ Convolutions are the fundamental building block of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it also is a major limitation, as it makes convolutions content agnostic.

We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are multiplied with a spatially-varying kernel that depends on learnable, local pixel features. PAC is a generalization of several popular filtering techniques and thus can be used for a wide range of use cases. Specifically, we demonstrate state-of-the-art performance when PAC is used for deep joint image upsampling. PAC also offers an effective alternative to fully-connected CRF (Full-CRF), called PAC-CRF, which performs competitively, while being considerably faster. In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.

links
Nervanasys

Tensorflow