activity
20212026
most citedStatic and Dynamic Synthesis of Bengali and Devanagari Signatures

30 citations · 39 across the 12 of their papers we have counts for

collaborators

12 papers

stat.ML2026

A statistical approach to bias in zero-shot learning: the lens of handwriting recognition

Clarence Chew, Gim Siang Chia, Sukalpa Chanda +2

Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were not observed during training. Tr…

cs.CV2024★ 30 cited

Static and Dynamic Synthesis of Bengali and Devanagari Signatures

Miguel A. Ferrer, Sukalpa Chanda, Moises Diaz +5

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging top…

cs.CV2024★ 4 cited

EZ-CLIP: Efficient Zeroshot Video Action Recognition

Shahzad Ahmad, Sukalpa Chanda, Yogesh S Rawat

Recent advancements in large-scale pre-training of visual-language models on paired image-text data have demonstrated impressive generalization capabilities for zero-shot tasks. Bu…

eess.IV2021

PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation

Abhishek Srivastava, Sukalpa Chanda, Debesh Jha +4

Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing t…

eess.IV2021

GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation

Abhishek Srivastava, Sukalpa Chanda, Debesh Jha +2

Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to…

cs.CV2021

Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification

Abhishek Srivastava, Sukalpa Chanda, Umapada Pal

Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier write…

cs.CV2021

AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination

Abhishek Srivastava, Sukalpa Chanda, Umapada Pal

The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural netwo…

cs.CV2021

LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning

Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya +2

Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc. In order to aid the training process, exi…

cs.CV2021★ 1 cited

Semantic Text-to-Face GAN -ST^2FG

Manan Oza, Sukalpa Chanda, David Doermann

Faces generated using generative adversarial networks (GANs) have reached unprecedented realism. These faces, also known as "Deep Fakes", appear as realistic photographs with very…

cs.CV2021

PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

Dipayan Das, Saumik Bhattacharya, Umapada Pal +1

Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the…

cs.CV2021★ 1 cited

Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition

Ravi Bhatt, Anuj Rai, Narayanan C. Krishnan +1

Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life…

eess.IV2021★ 3 cited

MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

Abhishek Srivastava, Debesh Jha, Sukalpa Chanda +6

Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of…