4 citations · 4 across the 10 of their papers we have counts for
12 papers · 1 filter
COT-AD: Cotton Analysis Dataset
Akbar Ali, Mahek Vyas, Soumyaratna Debnath +7
This paper presents COT-AD, a comprehensive Dataset designed to enhance cotton crop analysis through computer vision. Comprising over 25,000 images captured throughout the cotton g…
ObjMST: An Object-Focused Multimodal Style Transfer Framework
Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta
We propose ObjMST, an object-focused multimodal style transfer framework that provides separate style supervision for salient objects and surrounding elements while addressing alig…
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning
Prajwal Singh, Gautam Vashishtha, Indra Deep Mastan +1
The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Le…
SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image Segmentation
Chanda Grover Kamra, Indra Deep Mastan, Nitin Kumar +1
Recent developments in self-supervised learning (SSL) have made it possible to learn data representations without the need for annotations. Inspired by the non-contrastive SSL appr…
Sem-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta
CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requiring a reference style image). However, the ground semanti…
SEM-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
Chanda G Kamra, Indra Deep Mastan, Debayan Gupta
CLIPStyler demonstrated image style transfer with realistic textures using only the style text description (instead of requiring a reference style image). However, the ground seman…
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations
Chanda Grover, Indra Deep Mastan, Debayan Gupta
State-of-the-art empirical work has shown that visual representations learned by deep neural networks are robust in nature and capable of performing classification tasks on diverse…
FMD-cGAN: Fast Motion Deblurring using Conditional Generative Adversarial Networks
Jatin Kumar, Indra Deep Mastan, Shanmuganathan Raman
In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN delivers…
DeepObjStyle: Deep Object-based Photo Style Transfer
Indra Deep Mastan, Shanmuganathan Raman
One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input (style and content) images. An efficient strategy…
DILIE: Deep Internal Learning for Image Enhancement
Indra Deep Mastan, Shanmuganathan Raman
We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider t…
DeepCFL: Deep Contextual Features Learning from a Single Image
Indra Deep Mastan, Shanmuganathan Raman
Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These…
Blind Motion Deblurring through SinGAN Architecture
Harshil Jain, Rohit Patil, Indra Deep Mastan +1
Blind motion deblurring involves reconstructing a sharp image from an observation that is blurry. It is a problem that is ill-posed and lies in the categories of image restoration…