1 code implementation • CVPR 2022 • Mohit Goyal, Sahil Modi, Rishabh Goyal, Saurabh Gupta
Analyzing the hands shows what we can do to objects and how.
1 code implementation • 3 Feb 2021 • Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen
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.
no code implementations • 22 Dec 2019 • Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
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.
1 code implementation • CVPR 2018 • Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
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.