Search Results - "Badrinarayanan, Vijay"

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation by Badrinarayanan, Vijay, Kendall, Alex, Cipolla, Roberto

“…We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable…”
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Understanding RealWorld Indoor Scenes with Synthetic Data by Handa, Ankur, Patraucean, Viorica, Badrinarayanan, Vijay, Stent, Simon, Cipolla, Roberto

“…Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with…”
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RoomNet: End-to-End Room Layout Estimation by Chen-Yu Lee, Badrinarayanan, Vijay, Malisiewicz, Tomasz, Rabinovich, Andrew

“…This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of…”
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FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras by Hu, Anthony, Murez, Zak, Mohan, Nikhil, Dudas, Sofia, Hawke, Jeffrey, Badrinarayanan, Vijay, Cipolla, Roberto, Kendall, Alex

“…Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future…”
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Mixture of Trees Probabilistic Graphical Model for Video Segmentation by Badrinarayanan, Vijay, Budvytis, Ignas, Cipolla, Roberto

“…We present a novel mixture of trees probabilistic graphical model for semi-supervised video segmentation. Each component in this mixture represents a tree…”
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Semi-Supervised Video Segmentation Using Tree Structured Graphical Models by Badrinarayanan, V., Budvytis, I., Cipolla, R.

“…We present a novel patch-based probabilistic graphical model for semi-supervised video segmentation. At the heart of our model is a temporal tree structure…”
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SCORE: Exploiting Global Broadcasts to Create Offline Personal Channels for On-Demand Access by Nencioni, Gianfranco, Sastry, Nishanth, Tyson, Gareth, Badrinarayanan, Vijay, Karamshuk, Dmytro, Chandaria, Jigna, Crowcroft, Jon

“…The last 5 years have seen a dramatic shift in media distribution. For decades, TV and radio were solely provisioned using push-based broadcast technologies,…”
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Mixture of Trees Probabilistic Graphical Model for Video Segmentation: Tracking, Detection and Segmentation by BADRINARAYANAN, Vijay, BUDVYTIS, Ignas, CIPOLLA, Roberto

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Label propagation in video sequences by Badrinarayanan, V, Galasso, F, Cipolla, R

“…This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given…”
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Semi-supervised video segmentation using tree structured graphical models by Budvytis, I., Badrinarayanan, V., Cipolla, R.

“…We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which…”
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Semi-Supervised Video Segmentation Using Tree Structured Graphical Models by Badrinarayanan, Vijay, Budvytis, Ignas, Cipolla, Roberto

“…We present a novel patch-based probabilistic graphical model for semi-supervised video segmentation. At the heart of our model is a temporal tree structure…”
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Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues by Badrinarayanan, V., Perez, P., Le Clerc, F., Oisel, L.

“…We present a probabilistic multi-cue tracking approach constructed by employing a novel randomized template tracker and a constant color model based particle…”
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Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes by Phalak, Ameya, Badrinarayanan, Vijay, Rabinovich, Andrew

“…We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments. The proposed…”
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Linking vision and motion for self-supervised object-centric perception by Stocking, Kaylene C, Murez, Zak, Badrinarayanan, Vijay, Shotton, Jamie, Kendall, Alex, Tomlin, Claire, Burgess, Christopher P

“…Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features…”
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Reimagining an autonomous vehicle by Hawke, Jeffrey, E, Haibo, Badrinarayanan, Vijay, Kendall, Alex

“…The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development…”
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EyeNet: A Multi-Task Deep Network for Off-Axis Eye Gaze Estimation by Wu, Zhengyang, Rajendran, Srivignesh, Van As, Tarrence, Badrinarayanan, Vijay, Rabinovich, Andrew

“…Eye gaze estimation is a crucial component in Virtual and Mixed Reality. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the…”
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LingoQA: Visual Question Answering for Autonomous Driving by Marcu, Ana-Maria, Chen, Long, Hünermann, Jan, Karnsund, Alice, Hanotte, Benoit, Chidananda, Prajwal, Nair, Saurabh, Badrinarayanan, Vijay, Kendall, Alex, Shotton, Jamie, Arani, Elahe, Sinavski, Oleg

“…We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios,…”
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DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points by Sinha, Ayan, Murez, Zak, Bartolozzi, James, Badrinarayanan, Vijay, Rabinovich, Andrew

“…Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based…”
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FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras by Hu, Anthony, Murez, Zak, Mohan, Nikhil, Dudas, Sofía, Hawke, Jeffrey, Badrinarayanan, Vijay, Cipolla, Roberto, Kendall, Alex

“…Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future…”
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Estimating Depth from RGB and Sparse Sensing by Chen, Zhao, Badrinarayanan, Vijay, Drozdov, Gilad, Rabinovich, Andrew

“…In: European Conference on Computer Vision. pp. 176-192. Springer (2018) We present a deep model that can accurately produce dense depth maps given an RGB…”
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