105 citations · 148 across the 15 of their papers we have counts for
15 papers
Iterative Multi-granular Image Editing using Diffusion Models
K J Joseph, Prateksha Udhayanan, Tripti Shukla +4
Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such…
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, K J Joseph +3
While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are sev…
-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Chaitanya Devaguptapu, Samarth Sinha, K J Joseph +2
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model ove…
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Arjun Ashok, K J Joseph, Vineeth Balasubramanian
In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the mod…
Novel Class Discovery without Forgetting
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. I…
Spacing Loss for Discovering Novel Categories
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances fro…
Energy-based Latent Aligner for Incremental Learning
K J Joseph, Salman Khan, Fahad Shahbaz Khan +2
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks…
OW-DETR: Open-world Detection Transformer
Akshita Gupta, Sanath Narayan, K J Joseph +3
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown o…
Towards Open World Object Detection
K J Joseph, Salman Khan, Fahad Shahbaz Khan +1
Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when t…
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…
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…
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…
Submodular Batch Selection for Training Deep Neural Networks
K J Joseph, Vamshi Teja R, Krishnakant Singh +1
Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodu…
C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis
K J Joseph, Arghya Pal, Sailaja Rajanala +1
Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All exist…
MASON: A Model AgnoStic ObjectNess Framework
K J Joseph, Vineeth N Balasubramanian
This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision. The proposed method 'MASON' (Model-AgnoStic O…