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[post_content] => Dear All,
The Department of Computer Science invites you to the PhD Thesis Defense Seminar of our PhD Scholar Chanda Grover.
Abstract: Recent progress in deep learning has demonstrated the power of large-scale models for various tasks such as vision-language alignment, image segmentation, and image style transfer. Despite these successes, such models often fail to preserve semantic meaning, leading to weak fine-grained correspondence between images and text, inaccurate object grouping in segmentation, and over-stylisation or misplaced visual effects in style transfer.
In this seminar, we will discuss DeepAlign, a framework designed to address these challenges. The first work improves how models align images and text by making them aware of contextual cues. The second enhances unsupervised segmentation by better capturing object-level relationships. Extending to style transfer: the third work leverages semantic-aware foreground and background constraints for text-driven artistic style transfer, and the fourth work ensures semantic consistency during multimodal stylization that respects the distinct objects within an image.
Together, these contributions establish DeepAlign as a unifying framework for semantic preservation across three tasks. The work opens new directions for building semantically aware multimodal systems with broad applications in vision-language modelling, unsupervised segmentation, and creative AI.
About the Speaker: Chanda is a Ph.D. student in the Department of Computer Science under the supervision of Dr. Debayan Gupta and the co-supervision of Dr. Indra Deep Mastan (IIT BHU). Her thesis centers on developing techniques for cross-modal similarity modelling and exploring their applications. Her broader research interests include Computer Vision, Multimodal Learning, Robust Representation Learning, and Generative AI.
We look forward to your active participation.
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[post_date] => 2026-05-01 12:49:23
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[post_content] => Title: "Simplifying Usage &/or Prototyping of Interactions Methods: Tactile, Tangible, and Technologically Immersive"
Abstract: Human–Computer Interaction (HCI) continues to evolve toward more immersive, context-aware, and embodied experiences. However, prototyping many such interactions and experiences using currently explored methods often requires complex tools, technical expertise, and significant development time, and may also be associated with practical challenges such as bulkiness, portability constraints, and the need for device enhancements by users. This talk, “Simplifying Prototyping of Interactions: Tactile, Tangible, and Technologically Immersive,” presents research focused on enabling lightweight, accessible strategies for creating rich interactive experiences across diverse contexts.
Abhijeet’s work explores approaches that lower barriers from both prototyping and usability perspectives while preserving the quality of user experience. By combining rapid prototyping methods, low-cost materials, and adaptable technological frameworks, the presentation highlights how simplifying tools and processes can accelerate innovation, foster creativity, and support context-sensitive product/technique usage. Overall, the overarching goal of his research is to make immersive interaction development and its usage more approachable, scalable, and sustainable across academic, research, and real-world environments.
About the Speaker: Abhijeet Mishra is a researcher in the Human-Computer Interaction (HCI) area at IIIT-Delhi, focusing on the design of haptics for VR/Smartphones, construction toolkits for interactive storytelling, Tangible User Interfaces for expressive communication, and intuitive interaction with smart devices. His work explores the interplay between physical and digital realms to design and engineer immersive and interactive devices/interactions. Abhijeet holds a PhD in HCI area from IIIT-Delhi, where he focused on fostering and introducing a simplified prototyping approach to enhance design accessibility in haptic devices and physical interaction development. His work has been published in premier venues such as IEEE Transactions on Visualization and Computer Graphics (TVCG) and SIGCHI UbiComp (IMWUT) along with multiple patents. His current research focuses on understanding users' interactions with emerging digital devices, and exploring and prototyping innovative tangible interfaces/interactions and haptic systems.
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[post_content] => Dear All,
The Department of Computer Science invites you to the PhD Thesis Defense Seminar of our PhD Scholar Chanda Grover.
Abstract: Recent progress in deep learning has demonstrated the power of large-scale models for various tasks such as vision-language alignment, image segmentation, and image style transfer. Despite these successes, such models often fail to preserve semantic meaning, leading to weak fine-grained correspondence between images and text, inaccurate object grouping in segmentation, and over-stylisation or misplaced visual effects in style transfer.
In this seminar, we will discuss DeepAlign, a framework designed to address these challenges. The first work improves how models align images and text by making them aware of contextual cues. The second enhances unsupervised segmentation by better capturing object-level relationships. Extending to style transfer: the third work leverages semantic-aware foreground and background constraints for text-driven artistic style transfer, and the fourth work ensures semantic consistency during multimodal stylization that respects the distinct objects within an image.
Together, these contributions establish DeepAlign as a unifying framework for semantic preservation across three tasks. The work opens new directions for building semantically aware multimodal systems with broad applications in vision-language modelling, unsupervised segmentation, and creative AI.
About the Speaker: Chanda is a Ph.D. student in the Department of Computer Science under the supervision of Dr. Debayan Gupta and the co-supervision of Dr. Indra Deep Mastan (IIT BHU). Her thesis centers on developing techniques for cross-modal similarity modelling and exploring their applications. Her broader research interests include Computer Vision, Multimodal Learning, Robust Representation Learning, and Generative AI.
We look forward to your active participation.
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