no code implementations • 6 Nov 2024 • Siddharth Seth, Akash Sonth, Anirban Chakraborty
Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras.
1 code implementation • 28 Sep 2024 • Anirban Chakraborty, Matthias Katzfuss
Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial field based on relatively few training samples.
no code implementations • 28 Jul 2024 • Vikash Kumar, Himanshu Patil, Rohit Lal, Anirban Chakraborty
Most of the Unsupervised Domain Adaptation (UDA) algorithms focus on reducing the global domain shift between labelled source and unlabelled target domains by matching the marginal distributions under a small domain gap assumption.
1 code implementation • 18 Apr 2024 • Nakul Sharma, Aditay Tripathi, Anirban Chakraborty, Anand Mishra
In this work, we study the task of sketch-guided image inpainting.
no code implementations • 14 Sep 2023 • Anirban Chakraborty, Abhisek Chakraborty
Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification.
2 code implementations • 10 Sep 2023 • Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.
no code implementations • 31 May 2023 • M. Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan, Anirban Chakraborty
In practice, there can often be substantial heterogeneity (e. g., class imbalance) across the local data distributions observed by each of these clients.
no code implementations • 15 Mar 2023 • Aditay Tripathi, Anand Mishra, Anirban Chakraborty
and Sketchy datasets, respectively, and a $12. 2\%$ improvement in AP@50 for large objects that are `unseen' during training.
no code implementations • CVPR 2023 • Aditay Tripathi, Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy
We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures.
no code implementations • 1 Dec 2022 • Aditay Tripathi, Rajath R Dani, Anand Mishra, Anirban Chakraborty
In such a scenario, a hand-drawn sketch of the object could be a choice for a query.
no code implementations • 14 Nov 2022 • Aditay Tripathi, Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy
We also obtain gains of up to 28% and 8. 5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViT-B) and ImageNet-R (for ViT-S), respectively.
1 code implementation • 7 Nov 2022 • Vikash Kumar, Rohit Lal, Himanshu Patil, Anirban Chakraborty
The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing.
1 code implementation • 3 Nov 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden.
1 code implementation • 3 Nov 2022 • Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal, Anirban Chakraborty
At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy.
no code implementations • 3 Nov 2022 • Aditay Tripathi, Anand Mishra, Anirban Chakraborty
In VL-MPAG Net, we first construct a directed graph with object proposals as nodes and an edge between a pair of nodes representing a plausible relation between them.
no code implementations • 17 Oct 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Existing works use this technique to provably secure a pretrained non-robust model by training a custom denoiser network on entire training data.
no code implementations • 5 May 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal, Himanshu Patil, Anirban Chakraborty
We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i. e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary.
no code implementations • NeurIPS 2021 • Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target.
Ranked #6 on Unsupervised 3D Human Pose Estimation on Human3.6M
no code implementations • 4 Apr 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Deep models are highly susceptible to adversarial attacks.
no code implementations • CVPR 2022 • Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations.
Ranked #9 on Unsupervised 3D Human Pose Estimation on Human3.6M
Monocular 3D Human Pose Estimation Unsupervised 3D Human Pose Estimation +2
1 code implementation • 9 Nov 2021 • Chaitra Jambigi, Ruchit Rawal, Anirban Chakraborty
Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and gallery images come from different modalities.
no code implementations • 27 Oct 2021 • Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri, Anirban Chakraborty
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student).
no code implementations • 26 Oct 2021 • Gaurav Kumar Nayak, Het Shah, Anirban Chakraborty
Thus, in this work, we propose a novel problem of "Incremental Learning for Animal Pose Estimation".
no code implementations • 15 Jan 2021 • Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain, Anirban Chakraborty
We dub them "Data Impressions", which act as proxy to the training data and can be used to realize a variety of tasks.
1 code implementation • 24 Dec 2020 • Anirban Chakraborty, Manjari Bagchi
By comparing the distributions of various parameters of synthetic pulsars detectable by the Parkes Multibeam Pulsar Survey, the Pulsar Arecibo L-band Feed Array Survey, and two Swinburne Multibeam surveys with those of the real pulsars detected by the same surveys, we find that a good and physically realistic model can be obtained by using a uniform distribution of the braking index in the range of 2. 5 to 3. 0, a uniform distribution of the cosine of the angle between the spin and the magnetic axis in the range of 0 to 1, a log-normal birth distribution of the surface magnetic field with the mean and the standard deviation as 12. 85 and 0. 55 respectively while keeping the distributions of other parameters unchanged from the ones most commonly used in the literature.
High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
no code implementations • 29 Nov 2020 • Shreya Roy, Anirban Chakraborty
We will dive into the recent evolution of the deep models in the context of SISR over the past few years and will present a comparative study between these models.
no code implementations • 18 Nov 2020 • Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty
In such scenarios, existing approaches either iteratively compose a synthetic set representative of the original training dataset, one sample at a time or learn a generative model to compose such a transfer set.
1 code implementation • ECCV 2020 • Aditay Tripathi, Rajath R Dani, Anand Mishra, Anirban Chakraborty
We refer to this problem as sketch-guided object localization.
no code implementations • 3 Aug 2020 • Gaurav Kumar Nayak, Saksham Jain, R. Venkatesh Babu, Anirban Chakraborty
In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery.
no code implementations • 28 Jun 2020 • Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones
An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized 'points-of-interest' (POIs) to a user, if it can extract information from the user's preference history (exploitation) and effectively blend it with the user's current contextual information (exploration) to predict a POI's 'appropriateness' in the current context.
no code implementations • 24 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.
no code implementations • CVPR 2020 • Jogendra Nath Kundu, Siddharth Seth, Varun Jampani, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty
Camera captured human pose is an outcome of several sources of variation.
no code implementations • 14 Mar 2020 • Surbhi Aggarwal R., Venkatesh Babu, Anirban Chakraborty
Text-based person search aims to retrieve the pedestrian images that best match a given text query.
Ranked #19 on Text based Person Retrieval on CUHK-PEDES
no code implementations • 13 Mar 2020 • Shreya Roy, Anirban Chakraborty
We first perform Semantic Segmentation on the fully labeled isotropic biomedical source data (FIBSEM) and try to incorporate the the trained model for segmenting the target unlabelled dataset(SNEMI3D)which shares some similarities with the source dataset in the context of different types of cellular bodies and other cellular components.
no code implementations • 27 Dec 2019 • Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, R. Venkatesh Babu
We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples from a trained classifier, using a novel Data-enriching GAN (DeGAN) framework.
no code implementations • 21 Nov 2019 • Lakshmi Annamalai, Anirban Chakraborty, Chetan Singh Thakur
Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur.
no code implementations • 27 May 2019 • Anuj Pahuja, Avishek Majumder, Anirban Chakraborty, R. Venkatesh Babu
Segmenting salient objects in an image is an important vision task with ubiquitous applications.
1 code implementation • 20 May 2019 • Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu, Anirban Chakraborty
Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation.
no code implementations • 28 Sep 2018 • Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past.
no code implementations • 19 Jul 2018 • K L Navaneet, Ravi Kiran Sarvadevabhatla, Shashank Shekhar, R. Venkatesh Babu, Anirban Chakraborty
Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras.
1 code implementation • 5 Jun 2014 • Toufiq Parag, Anirban Chakraborty, Stephen Plaza, Lou Scheffer
In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron.