9.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR444 papers
- Institute for High Energy PhysicsES405 papers
- Stanford UniversityUS359 papers
- University of SouthamptonGB359 papers
- University College LondonGB358 papers
- University of MichiganUS356 papers
- Fermi National Accelerator LaboratoryUS350 papers
- University of ChicagoUS344 papers
- Ludwig-Maximilians-Universität MünchenDE323 papers
- University of Illinois Urbana-ChampaignUS315 papers
- Centro de Investigaciones Energéticas, Medioambientales y TecnológicasES312 papers
- The Ohio State UniversityUS305 papers
13 papers · 2 filters
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions
Radhika Dua, Sai Srinivas Kancheti, Vineeth N Balasubramanian
Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained…
Meta-Consolidation for Continual Learning
K J Joseph, Vineeth N Balasubramanian
The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep…
Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest
Satyam Mohla, Sidharth Mohla, Anupam Guha +1
Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscape…
Explainable Disease Classification via weakly-supervised segmentation
Aniket Joshi, Gaurav Mishra, Jayanthi Sivaswamy
Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to v…
Zero Shot Domain Generalization
Udit Maniyar, Joseph K J, Aniket Anand Deshmukh +2
Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that…
Assisting Scene Graph Generation with Self-Supervision
Sandeep Inuganti, Vineeth N Balasubramanian
Research in scene graph generation has quickly gained traction in the past few years because of its potential to help in downstream tasks like visual question answering, image capt…
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning
Shivam Chandhok, Vineeth N Balasubramanian
The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semanti…
On the Demystification of Knowledge Distillation: A Residual Network Perspective
Nandan Kumar Jha, Rajat Saini, Sparsh Mittal
Knowledge distillation (KD) is generally considered as a technique for performing model compression and learned-label smoothing. However, in this paper, we study and investigate th…
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
Rajat Saini, Nandan Kumar Jha, Bedanta Das +2
The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention off…
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Surgan Jandial, Ayush Chopra, Mausoom Sarkar +3
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains. In this work, we introduce a new retrospective loss to improve the tr…
RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
Sangeeth Reddy, Minesh Mathew, Lluis Gomez +3
Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most o…
Incremental Object Detection via Meta-Learning
K J Joseph, Jathushan Rajasegaran, Salman Khan +2
In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their…
Towards Automatic Face-to-Face Translation
Prajwal K R, Rudrabha Mukhopadhyay, Jerin Philip +3
In light of the recent breakthroughs in automatic machine translation systems, we propose a novel approach that we term as "Face-to-Face Translation". As today's digital communicat…