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Amandeep Kumar

5 accepted papers

2025

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana, Noor Ahsan, Nevasini Sasikumar, Omkar Thawakar +63
CVPR 2025highlight

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corr…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗
2023

Generative Multiplane Neural Radiance for 3D-Aware Image Generation

Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan +2
ICCV 2023poster

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, named GMNR, consists of a novel a-guided view-dependent representation (a-VdR) module for learning view-dependent informat…

Cited by 3PDF ↗code ↗Scholar ↗
2023

Sketch2Saliency: Learning To Detect Salient Objects From Human Drawings

Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang +1
CVPR 2023poster

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 sketches -- that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More spec…

Cited by 25Source ↗PDF ↗Scholar ↗
2021

Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition

Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar, Shuvozit Ghose, Pinaki Nath Chowdhury, Yi-Zhe Song
ICCV 2021poster

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, uncontrolled illuminations, distortions and other artifacts. This is because such models solely depend on visual informat…

Cited by 80PDF ↗Scholar ↗
2021

MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition

Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song
CVPR 2021poster

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 with the assumption that there is a limited number of styles, most of which have already been captured by existing datasets…

Cited by 43PDF ↗Scholar ↗