1 citations · 1 across the 4 of their papers we have counts for
4 papers
OLAF: A Plug-and-Play Framework for Enhanced Multi-object Multi-part Scene Parsing
Pranav Gupta, Rishubh Singh, Pradeep Shenoy +1
Multi-object multi-part scene segmentation is a challenging task whose complexity scales exponentially with part granularity and number of scene objects. To address the task, we pr…
Robustifying Deep Vision Models Through Shape Sensitization
Aditay Tripathi, Rishubh Singh, Anirban Chakraborty +1
Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies h…
FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing
Rishubh Singh, Pranav Gupta, Pradeep Shenoy +1
Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In thi…
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
Gantavya Bhatt, Hritik Bansal, Rishubh Singh +1
Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic t…