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Full-Text Articles in Computer Engineering
Ki-Cook: Clustering Multimodal Cooking Representations Through Knowledge-Infused Learning, Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Ronak Kaoshik, Anirudh Sundara Rajan, Amit Sheth
Ki-Cook: Clustering Multimodal Cooking Representations Through Knowledge-Infused Learning, Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Ronak Kaoshik, Anirudh Sundara Rajan, Amit Sheth
Publications
Cross-modal recipe retrieval has gained prominence due to its ability to retrieve a text representation given an image representation and vice versa. Clustering these recipe representations based on similarity is essential to retrieve relevant information about unknown food images. Existing studies cluster similar recipe representations in the latent space based on class names. Due to inter-class similarity and intraclass variation, associating a recipe with a class name does not provide sufficient knowledge about recipes to determine similarity. However, recipe title, ingredients, and cooking actions provide detailed knowledge about recipes and are a better determinant of similar recipes. In this study, …
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
Mathematics, Physics, and Computer Science Faculty Articles and Research
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised person ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, this thesis proposes a new iterative clustering framework that incorporates (a) two attention architectures that learn …
Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek
Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …
Distributed Object Tracking Using A Cluster-Based Kalman Filter In Wireless Camera Networks, Henry Medeiros, Johnny Park, Avinash Kak
Distributed Object Tracking Using A Cluster-Based Kalman Filter In Wireless Camera Networks, Henry Medeiros, Johnny Park, Avinash Kak
Electrical and Computer Engineering Faculty Research and Publications
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target …