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Found 4 papers, 3 papers with code
Human Hands as Probes for Interactive Object Understanding
Analyzing the hands shows what we can do to objects and how.
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms
One drawback however is the high computational complexity and high memory consumption of CNNs which makes them unfeasible for execution on embedded platforms which are constrained on physical resources needed to support CNNs.
Neural Shape Parsers for Constructive Solid Geometry
We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.
CSGNet: Neural Shape Parser for Constructive Solid Geometry
In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.
