Tanima Dutta
Biographic Data
| ID | 6094974 |
|---|---|
| NAME | Tanima Dutta |
| GIVEN NAMES | Tanima |
| FAMILY NAME | Dutta |
| SIGNATURE | DUTTA T |
| AFFILIATIONS | Indian Institute of Technology BHU |
| ORCID | 0000-0002-2801-0687 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Meta-Adaptive Continual Learning for Dynamic Social Communication Graphs
The rapid and continuous evolution of user interactions in social communication systems such as social media platforms, messaging applications, and collaborative content networks creates highly dynamic graph structures. These evolving environments present a significant challenge for accurately modeling and understanding user behaviors, which is essential for tasks such as personalized recommendations, community detection, and social influence ana…
Enhancing User Engagement Through Contextual Video Captioning With Social and Behavior Insights
In the era of social media, accurate and contextually rich captions are essential for enhancing user engagement with video content. Traditional captioning methods, relying solely on visual content, often fall short of capturing audience interest. To address this, advanced captioning solutions are needed to combine visual cues with contextual insights. This article proposes a novel captioning approach that incorporates contextual information, lead…
Analysis of determinants of groundwater depletion in rice-wheat ecosystems of Punjab- a case of India
Emotion and Gesture Guided Action Recognition in Videos Using Supervised Deep Networks
Emotions and gestures are essential elements in improving social intelligence and predicting real human action. In recent years, recognition of human visual actions using deep neural networks (DNNs) has gained wide popularity in multimedia and computer vision. However, ambiguous action classes, such as “praying” and “pleading,” are still challenging to classify due to similar visual cues of action. We need to focus on attentive associated feature…
Supervised Attention Network for Arbitrary-Shaped Text Detection in Edge-Fainted Noisy Scene Images
Text mining in noisy situations, like poor contrast and fainted edges, is one of the challenging areas of research in the domain of social networks and computer vision. Scene text detection is a complicated task as text regionhaving varying span in term of size, orientation, aspect ratio, color, font, and script. Furthermore, the contrast of a scene image varies drastically in noisy situations due to poor illumination and image filtering. This fa…
Notes and Correspondence
Edited Collection on Posthumanism
Analysis, Modeling, and Representation of Covid-19 Spread
Coronavirus outbreak is one of the challenging pandemics for the entire human population on Earth. Techniques, such as the isolation of infected people and maintaining social distancing, are the only preventive measures against the pandemic. The actual estimation of the number of infected peoples with limited data is an indeterminate problem faced by data scientists. There are several techniques in the existing literature, including reproduction …
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Analysis, Modeling, and Representation of Covid-19 Spread
Coronavirus outbreak is one of the challenging pandemics for the entire human population on Earth. Techniques, such as the isolation of infected people and maintaining social distancing, are the only preventive measures against the pandemic. The actual estimation of the number of infected peoples with limited data is an indeterminate problem faced by data scientists. There are several techniques in the existing literature, including reproduction …
Emotion and Gesture Guided Action Recognition in Videos Using Supervised Deep Networks
Emotions and gestures are essential elements in improving social intelligence and predicting real human action. In recent years, recognition of human visual actions using deep neural networks (DNNs) has gained wide popularity in multimedia and computer vision. However, ambiguous action classes, such as “praying” and “pleading,” are still challenging to classify due to similar visual cues of action. We need to focus on attentive associated feature…
Supervised Attention Network for Arbitrary-Shaped Text Detection in Edge-Fainted Noisy Scene Images
Text mining in noisy situations, like poor contrast and fainted edges, is one of the challenging areas of research in the domain of social networks and computer vision. Scene text detection is a complicated task as text regionhaving varying span in term of size, orientation, aspect ratio, color, font, and script. Furthermore, the contrast of a scene image varies drastically in noisy situations due to poor illumination and image filtering. This fa…
Notes and Correspondence
Edited Collection on Posthumanism
Analysis of determinants of groundwater depletion in rice-wheat ecosystems of Punjab- a case of India
Enhancing User Engagement Through Contextual Video Captioning With Social and Behavior Insights
In the era of social media, accurate and contextually rich captions are essential for enhancing user engagement with video content. Traditional captioning methods, relying solely on visual content, often fall short of capturing audience interest. To address this, advanced captioning solutions are needed to combine visual cues with contextual insights. This article proposes a novel captioning approach that incorporates contextual information, lead…
Meta-Adaptive Continual Learning for Dynamic Social Communication Graphs
The rapid and continuous evolution of user interactions in social communication systems such as social media platforms, messaging applications, and collaborative content networks creates highly dynamic graph structures. These evolving environments present a significant challenge for accurately modeling and understanding user behaviors, which is essential for tasks such as personalized recommendations, community detection, and social influence ana…
Computer Science (3 works) · Artificial Intelligence (2 works) · Biology (2 works) · Discriminative model (2 works) · Geography (2 works) · Multimodal Machine Learning Applications (2 works) · Advanced Graph Neural Networks (1 work) · Agricultural Economics and Practices (1 work) · Agroforestry (1 work) · Art (1 work)