Search Results for author: Jogendra Nath Kundu

Found 23 papers, 11 papers with code

Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

1 code implementation27 Jul 2022 Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni, Hiran Sarkar, Varun Jampani, R. Venkatesh Babu

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains.

Unsupervised Domain Adaptation

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

no code implementations16 Jun 2022 Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Kulkarni, Varun Jampani, R. Venkatesh Babu

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.

Domain Adaptation Semantic Segmentation

Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery

no code implementations NeurIPS 2021 Mugalodi Rakesh, Jogendra Nath Kundu, Varun Jampani, R. Venkatesh Babu

Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques.

3D Human Pose Estimation

Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation

no code implementations9 Feb 2022 Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri, Varun Jampani, R. Venkatesh Babu

However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination.

Disentanglement Domain Adaptation +1

Appearance Consensus Driven Self-Supervised Human Mesh Recovery

no code implementations ECCV 2020 Jogendra Nath Kundu, Mugalodi Rakesh, Varun Jampani, Rahul Mysore Venkatesh, R. Venkatesh Babu

We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision.

3D Pose Estimation Human Mesh Recovery

Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation

no code implementations24 Jun 2020 Jogendra Nath Kundu, Siddharth Seth, Rahul M. V, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty

However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments.

3D Human Pose Estimation 3D Pose Estimation +2

Towards Inheritable Models for Open-Set Domain Adaptation

1 code implementation CVPR 2020 Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M. V, R. Venkatesh Babu

Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future.

Domain Adaptation

Universal Source-Free Domain Adaptation

1 code implementation CVPR 2020 Jogendra Nath Kundu, Naveen Venkat, Rahul M. V, R. Venkatesh Babu

1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.

Domain Adaptation

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation

1 code implementation ICCV 2019 Jogendra Nath Kundu, Nishank Lakkakula, R. Venkatesh Babu

In this paper, we propose UM-Adapt - a unified framework to effectively perform unsupervised domain adaptation for spatially-structured prediction tasks, simultaneously maintaining a balanced performance across individual tasks in a multi-task setting.

Multi-Task Learning Representation Learning +2

GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions

2 code implementations ICCV 2019 Jogendra Nath Kundu, Maharshi Gor, Dakshit Agrawal, R. Venkatesh Babu

Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions.

Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold

2 code implementations6 Dec 2018 Jogendra Nath Kundu, Maharshi Gor, Phani Krishna Uppala, R. Venkatesh Babu

In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner.

Fine-grained Action Recognition Representation Learning

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

2 code implementations6 Dec 2018 Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu

The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence.

Human motion prediction motion prediction

Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence

2 code implementations3 Sep 2018 Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan, R. Venkatesh Babu

In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation.

Pose Estimation Viewpoint Estimation

iSPA-Net: Iterative Semantic Pose Alignment Network

2 code implementations3 Aug 2018 Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V., Aditya Prakash, R. Venkatesh Babu

Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.

Pose Estimation Semantic correspondence +1

AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

no code implementations CVPR 2018 Jogendra Nath Kundu, Phani Krishna Uppala, Anuj Pahuja, R. Venkatesh Babu

Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies.

Monocular Depth Estimation Unsupervised Domain Adaptation

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