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6. GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR

Author

Singh, Bharat, Kulharia, Viveka, Yang, Luyu, Ravichandran, Avinash, Tyagi, Ambrish, and Shrivastava, Ashish

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail. We propose a novel approach, GenMM, for jointly editing RGB videos and LiDAR scans by inserting temporally and geometrically consistent 3D objects. Our method uses a reference image and 3D bounding boxes to seamlessly insert and blend new objects into target videos. We inpaint the 2D Regions of Interest (consistent with 3D boxes) using a diffusion-based video inpainting model. We then compute semantic boundaries of the object and estimate it's surface depth using state-of-the-art semantic segmentation and monocular depth estimation techniques. Subsequently, we employ a geometry-based optimization algorithm to recover the 3D shape of the object's surface, ensuring it fits precisely within the 3D bounding box. Finally, LiDAR rays intersecting with the new object surface are updated to reflect consistent depths with its geometry. Our experiments demonstrate the effectiveness of GenMM in inserting various 3D objects across video and LiDAR modalities.

Published
2024

10. Engineered probiotic overcomes pathogen defences using signal interference and antibiotic production to treat infection in mice.

Author

Do, Hackwon, Li, Zhong-Rui, Tripathi, Praveen, Mitra, Sonali, Guerra, Stephanie, Dash, Ananya, Weerasekera, Dulanthi, Makthal, Nishanth, Shams, Syed, Aggarwal, Shifu, Singh, Bharat, Du, Yongle, Olsen, Randall, LaRock, Christopher, Kumaraswami, Muthiah, Zhang, Wenjun, and Gu, Di

Subjects
Animals, Humans, Mice, Anti-Bacterial Agents, Streptococcal Infections, Streptococcus pyogenes, Saliva, Probiotics
Abstract

Probiotic supplements are suggested to promote human health by preventing pathogen colonization. However, the mechanistic bases for their efficacy in vivo are largely uncharacterized. Here using metabolomics and bacterial genetics, we show that the human oral probiotic Streptococcus salivarius K12 (SAL) produces salivabactin, an antibiotic that effectively inhibits pathogenic Streptococcus pyogenes (GAS) in vitro and in mice. However, prophylactic dosing with SAL enhanced GAS colonization in mice and ex vivo in human saliva. We showed that, on co-colonization, GAS responds to a SAL intercellular peptide signal that controls SAL salivabactin production. GAS produces a secreted protease, SpeB, that targets SAL-derived salivaricins and enhances GAS survival. Using this knowledge, we re-engineered probiotic SAL to prevent signal eavesdropping by GAS and potentiate SAL antimicrobials. This engineered probiotic demonstrated superior efficacy in preventing GAS colonization in vivo. Our findings show that knowledge of interspecies interactions can identify antibiotic- and probiotic-based strategies to combat infection.

Published
2024

17. LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation

Author

Pnvr, Koutilya, Singh, Bharat, Ghosh, Pallabi, Siddiquie, Behjat, and Jacobs, David

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Large-scale pre-training tasks like image classification, captioning, or self-supervised techniques do not incentivize learning the semantic boundaries of objects. However, recent generative foundation models built using text-based latent diffusion techniques may learn semantic boundaries. This is because they have to synthesize intricate details about all objects in an image based on a text description. Therefore, we present a technique for segmenting real and AI-generated images using latent diffusion models (LDMs) trained on internet-scale datasets. First, we show that the latent space of LDMs (z-space) is a better input representation compared to other feature representations like RGB images or CLIP encodings for text-based image segmentation. By training the segmentation models on the latent z-space, which creates a compressed representation across several domains like different forms of art, cartoons, illustrations, and photographs, we are also able to bridge the domain gap between real and AI-generated images. We show that the internal features of LDMs contain rich semantic information and present a technique in the form of LD-ZNet to further boost the performance of text-based segmentation. Overall, we show up to 6% improvement over standard baselines for text-to-image segmentation on natural images. For AI-generated imagery, we show close to 20% improvement compared to state-of-the-art techniques. The project is available at https://koutilya-pnvr.github.io/LD-ZNet/., Comment: Supplementary material is included in the paper following the references section

Published
2023

20. Optimal Energy Scheduling and Feasibility Analysis in Microgrid Considering the Hospital Load Model with Isolated Grid

Author

Singh, Bharat, Kumar, Ashwani, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Ashwani, editor, Singh, S. N., editor, and Kumar, Pradeep, editor

Published
2024
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21. Aspect Level Sentiment Analysis to Extract Valuable Insight for Airline’s Customer Feedback and Reviews

Author

Singh, Bharat, Kushwaha, Nidhi, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Pareek, Prakash, editor, Gupta, Nishu, editor, and Reis, M. J. C. S., editor

Published
2024
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22. Investigation of Control Algorithm for PMSM-Based Electric Vehicle Using Vehicle Dynamics

Author

Singh, Bharat, Chowdhury, Ankur, Mishra, Vishal, Jain, Ankur, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Goyal, Sunil Kumar, editor, Palwalia, Dheeraj Kumar, editor, Tiwari, Rajiv, editor, and Gupta, Yeshpal, editor

Published
2024
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23. Design and Control of Battery Management System for Electric Vehicle

Author

Singh, Bharat, Rawat, Deepanshu, Parwani, Pulkesh, Gupta, Rhydham, Kapoor, Tisha, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Goyal, Sunil Kumar, editor, Palwalia, Dheeraj Kumar, editor, Tiwari, Rajiv, editor, and Gupta, Yeshpal, editor

Published
2024
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50. Scale Normalized Image Pyramids with AutoFocus for Object Detection

Author

Singh, Bharat, Najibi, Mahyar, Sharma, Abhishek, and Davis, Larry S.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

We present an efficient foveal framework to perform object detection. A scale normalized image pyramid (SNIP) is generated that, like human vision, only attends to objects within a fixed size range at different scales. Such a restriction of objects' size during training affords better learning of object-sensitive filters, and therefore, results in better accuracy. However, the use of an image pyramid increases the computational cost. Hence, we propose an efficient spatial sub-sampling scheme which only operates on fixed-size sub-regions likely to contain objects (as object locations are known during training). The resulting approach, referred to as Scale Normalized Image Pyramid with Efficient Resampling or SNIPER, yields up to 3 times speed-up during training. Unfortunately, as object locations are unknown during inference, the entire image pyramid still needs processing. To this end, we adopt a coarse-to-fine approach, and predict the locations and extent of object-like regions which will be processed in successive scales of the image pyramid. Intuitively, it's akin to our active human-vision that first skims over the field-of-view to spot interesting regions for further processing and only recognizes objects at the right resolution. The resulting algorithm is referred to as AutoFocus and results in a 2.5-5 times speed-up during inference when used with SNIP., Comment: Accepted in T-PAMI 2021

Published
2021

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