85 citations · 133 across the 29 of their papers we have counts for
22 papers · 1 filter
Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs
Beining Xu, Hairui Wang, Jiaxin Wang +2
While the privacy risks of multimodal large language models (MLLMs) have drawn significant attention, the unique vulnerabilities of domain-specific MLLMs remain largely underexplor…
Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification
Siddharth Seth, Akash Sonth, Anirban Chakraborty
Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be di…
Improving Domain Adaptation Through Class Aware Frequency Transformation
Vikash Kumar, Himanshu Patil, Rohit Lal +1
In this work, we explore the usage of the Frequency Transformation for reducing the domain shift between the source and target domain (e.g., synthetic image and real image respecti…
Query-guided Attention in Vision Transformers for Localizing Objects Using a Single Sketch
Aditay Tripathi, Anand Mishra, Anirban Chakraborty
In this work, we investigate the problem of sketch-based object localization on natural images, where given a crude hand-drawn sketch of an object, the goal is to localize all the…
Multimodal Query-guided Object Localization
Aditay Tripathi, Rajath R Dani, Anand Mishra +1
Consider a scenario in one-shot query-guided object localization where neither an image of the object nor the object category name is available as a query. In such a scenario, a ha…
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…
Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing
Aditay Tripathi, Anand Mishra, Anirban Chakraborty
This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several…
Robust Few-shot Learning Without Using any Adversarial Samples
Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri +1
The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime importance. Existing works mainly focus on improving performance on clean data a…
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems
Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal +2
Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (rob…
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi +4
Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domai…
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM +3
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in…
MMD-ReID: A Simple but Effective Solution for Visible-Thermal Person ReID
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…
Beyond Classification: Knowledge Distillation using Multi-Object Impressions
Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri +1
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student). Several works have recen…
Incremental Learning for Animal Pose Estimation using RBF k-DPP
Gaurav Kumar Nayak, Het Shah, Anirban Chakraborty
Pose estimation is the task of locating keypoints for an object of interest in an image. Animal Pose estimation is more challenging than estimating human pose due to high inter and…
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data
Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain +1
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen…
Sketch-Guided Object Localization in Natural Images
Aditay Tripathi, Rajath R Dani, Anand Mishra +1
We introduce the novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch…
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images
Gaurav Kumar Nayak, Saksham Jain, R Venkatesh Babu +1
In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revi…
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Rahul M +3
Estimation of 3D human pose from monocular image has gained considerable attention, as a key step to several human-centric applications. However, generalizability of human pose est…
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Jogendra Nath Kundu, Siddharth Seth, Varun Jampani +3
Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, s…
Semantic Segmentation of highly class imbalanced fully labelled 3D volumetric biomedical images and unsupervised Domain Adaptation of the pre-trained Segmentation Network to segment another fully unlabelled Biomedical 3D Image stack
Shreya Roy, Anirban Chakraborty
The goal of our work is to perform pixel label semantic segmentation on 3D biomedical volumetric data. Manual annotation is always difficult for a large bio-medical dataset. So, we…
Enhancing Salient Object Segmentation Through Attention
Anuj Pahuja, Avishek Majumder, Anirban Chakraborty +1
Segmenting salient objects in an image is an important vision task with ubiquitous applications. The problem becomes more challenging in the presence of a cluttered and textured ba…
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification
K L Navaneet, Ravi Kiran Sarvadevabhatla, Shashank Shekhar +2
Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these…