activity
20192022
most citedStructured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains

2 citations · 2 across the 6 of their papers we have counts for

collaborators

8 papers

eess.SP2022

Hardware Software Co-design of Statistical and Deep Learning Frameworks for Wideband Sensing on Zynq System on Chip

Rohith Rajesh, Sumit J. Darak, Akshay Jain +2

With the introduction of spectrum sharing and heterogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum re…

cs.CV2022

Unseen Classes at a Later Time? No Problem

Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok +1

Recent progress towards learning from limited supervision has encouraged efforts towards designing models that can recognize novel classes at test time (generalized zero-shot learn…

eess.SP2021

Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers

Piyush Sahoo, Romesh Rajoria, Shivam Chandhok +3

With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency…

cs.CV2021

Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains

Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian +1

Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards…

cs.CV2021

Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision

Gaurav Bhatt, Shivam Chandhok, Vineeth N Balasubramanian

A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize…

cs.CV20212 cited

Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains

Shivam Chandhok, Sanath Narayan, Hisham Cholakkal +4

The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain…