4 citations · 15 across the 12 of their papers we have counts for
13 papers
Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
Daniya Najiha A. Kareem, Jean Lahoud, Mustansar Fiaz +2
Many practical medical imaging scenarios include categories that are under-represented but still crucial. The relevance of image recognition models to real-world applications lies…
AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
Muhammad Awais, Ali Husain Salem Abdulla Alharthi, Amandeep Kumar +2
Significant progress has been made in advancing large multimodal conversational models (LMMs), capitalizing on vast repositories of image-text data available online. Despite this p…
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt Learning
Amandeep Kumar, Muhammad Awais, Sanath Narayan +3
Drawing upon StyleGAN's expressivity and disentangled latent space, existing 2D approaches employ textual prompting to edit facial images with different attributes. In contrast, 3D…
Multi-modal Generation via Cross-Modal In-Context Learning
Amandeep Kumar, Muzammal Naseer, Sanath Narayan +3
In this work, we study the problem of generating novel images from complex multimodal prompt sequences. While existing methods achieve promising results for text-to-image generatio…
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes
Dmitry Demidov, Roba Al Majzoub, Amandeep Kumar +1
Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available…
Generative Multiplane Neural Radiance for 3D-Aware Image Generation
Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan +5
We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views. The proposed multiplane neural radiance model, na…
Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification
Amandeep Kumar, Ankan kumar Bhunia, Sanath Narayan +4
In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues. Our few-shot g…
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar +4
Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sket…
Transformers in Remote Sensing: A Survey
Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer +4
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures,…
Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts
Prema Satish Sharan, Sowmya Aitha, Amandeep Kumar +3
Handwritten documents are often characterized by dense and uneven layout. Despite advances, standard deep network based approaches for semantic layout segmentation are not robust t…
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar +3
Although text recognition has significantly evolved over the years, state-of-the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, u…
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar +3
Handwritten Text Recognition (HTR) remains a challenging problem to date, largely due to the varying writing styles that exist amongst us. Prior works however generally operate wit…
UDBNET: Unsupervised Document Binarization Network via Adversarial Game
Amandeep Kumar, Shuvozit Ghose, Pinaki Nath Chowdhury +2
Degraded document image binarization is one of the most challenging tasks in the domain of document image analysis. In this paper, we present a novel approach towards document imag…