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Suvaansh Bhambri

7 accepted papers

2023

Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation

Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri, Akshay Kulkarni, Jogendra Nath Kundu, R Venkatesh Babu
ICCV 2023poster

Conventional Domain Adaptation (DA) methods aim to learn domain-invariant feature representations to improve the target adaptation performance. However, we motivate that domain-specificity is equally important since in-domain trained models hold crucial domain-specific properties that are beneficial…

Cited by 15PDF ↗Scholar ↗
2023

Multi-Level Compositional Reasoning for Interactive Instruction Following

Suvaansh Bhambri, Byeonghwi Kim, Jonghyun Choi
AAAI 2023technical

Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are challenging as completing them require to reason abou…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2022

Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation

Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Varun Jampani, Venkatesh Babu Radhakrishnan
AAAI 2022technical

Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypothesize that an improved disentanglement of domain…

Cited by 12Source ↗PDF ↗Scholar ↗
2022

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani +1
ICML 2022spotlight

Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data. However, the requirement of simultaneous access to labeled sourc…

Cited by 107Source ↗PDF ↗code ↗Scholar ↗
2022

Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni, Hiran Sarkar, Varun Jampani, R. Venkatesh Babu
ECCV 2022poster

"The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2022

Subsidiary Prototype Alignment for Universal Domain Adaptation

Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Ravindra Kulkarni, Hiran Sarkar, Varun Jampani, Venkatesh Babu Radhakrishnan
NeurIPS 2022accept

Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in Uni…

Cited by 25Source ↗PDF ↗Scholar ↗
2021

Factorizing Perception and Policy for Interactive Instruction Following

Kunal Pratap Singh, Suvaansh Bhambri, Byeonghwi Kim, Roozbeh Mottaghi, Jonghyun Choi
ICCV 2021poster

Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for an AI agent. The 'interactive instruction following' task attempts to make progress towards building an agent that can jointly navigate, interact, and reason in the environm…

Cited by 41PDF ↗code ↗Scholar ↗