38 citations · 235 across the 22 of their papers we have counts for
10 papers · 1 filter
A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Shalini Dangi, Surya Karthikeya Mullapudi, Chandravardhan Singh Raghaw +3
Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors su…
An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities
Shahid Shafi Dar, Bharat Kaurav, Arnav Jain +3
In an era of escalating climate change, urban flooding has emerged as a critical challenge for sustainable cities, threatening lives, infrastructure, and ecosystems. Traditional fl…
A Multimodal-Multitask Framework with Cross-modal Relation and Hierarchical Interactive Attention for Semantic Comprehension
Mohammad Zia Ur Rehman, Devraj Raghuvanshi, Umang Jain +2
A major challenge in multimodal learning is the presence of noise within individual modalities. This noise inherently affects the resulting multimodal representations, especially w…
ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos
Mohammad Zia Ur Rehman, Anukriti Bhatnagar, Omkar Kabde +2
The existing research has primarily focused on text and image-based hate speech detection, video-based approaches remain underexplored. In this work, we introduce a novel dataset,…
A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection
Mohammad Zia Ur Rehman, Sufyaan Zahoor, Areeb Manzoor +2
A substantial portion of offensive content on social media is directed towards women. Since the approaches for general offensive content detection face a challenge in detecting mis…
T-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentation
Chandravardhan Singh Raghaw, Jasmer Singh Sanjotra, Mohammad Zia Ur Rehman +3
Precise and automated segmentation of the liver and its tumor within CT scans plays a pivotal role in swift diagnosis and the development of optimal treatment plans for individuals…
MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy
Chandravardhan Singh Raghaw, Aryan Yadav, Jasmer Singh Sanjotra +2
Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in pr…
CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection
Chandravardhan Singh Raghaw, Arnav Sharma, Shubhi Bansal +2
Swift and accurate blood smear analysis is an effective diagnostic method for leukemia and other hematological malignancies. However, manual leukocyte count and morphological evalu…
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis
Prasad Chaudhari, Aman Kumar, Chandravardhan Singh Raghaw +2
Sentiment analysis and emotion recognition in videos are challenging tasks, given the diversity and complexity of the information conveyed in different modalities. Developing a hig…
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Shubhi Bansal, Sreeharish A, Madhava Prasath J +6
Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are…