Sukthankar, G.
* 2000: Face Recognition: A Critical Look at Biologically-Inspired Approaches
* 2001: Dynamic Shadow Elimination for Multi-Projector Displays
* 2001: Self-Calibrating Camera Projector Systems for Interactive Displays and Presentations
* 2001: Shadow Elimination and Occluder Light Suppression for Multi-Projector Displays
* 2003: Shadow Elimination and Occluder Light Suppression for Multi-Projector Displays
* 2005: Activity Recognition for Physically-Embodied Agent Teams
* 2010: Motif Discovery and Feature Selection for CRF-based Activity Recognition
* 2012: Importance-weighted label prediction for active learning with noisy annotations
* 2022: Predicting Team Performance with Spatial Temporal Graph Convolutional Networks
Includes: Sukthankar, G. Sukthankar, G.[Gita]
9 for Sukthankar, G.
Sukthankar, R.[Rahul]
* 1993: Panacea: An Active Sensor Controller for the ALVINN Autonomous Driving System
* 2000: JKanji: Wavelet-based Interactive Kanji Completion
* 2000: Memory-based Face Recognition for Visitor Identification
* 2001: Dynamic Shadow Elimination for Multi-Projector Displays
* 2001: Self-Calibrating Camera Projector Systems for Interactive Displays and Presentations
* 2001: Shadow Elimination and Occluder Light Suppression for Multi-Projector Displays
* 2001: Smarter Presentations: Exploiting Homography in Camera-Projector Systems
* 2002: theory of the quasi-static world, A
* 2003: Shadow Elimination and Occluder Light Suppression for Multi-Projector Displays
* 2004: flexible projector-camera system for multi-planar displays, A
* 2004: Object-based image retrieval using the statistical structure of images
* 2004: PCA-SIFT: a more distinctive representation for local image descriptors
* 2005: Computer Vision for Music Identification
* 2005: Computer Vision for Music Identification: Video Demonstration
* 2005: Efficient Visual Event Detection Using Volumetric Features
* 2006: Correlated Label Propagation with Application to Multi-label Learning
* 2006: Industry and Object Recognition: Applications, Applied Research and Challenges
* 2006: Semantic Learning for Audio Applications: A Computer Vision Approach
* 2007: Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features
* 2007: Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data
* 2007: Event Detection in Crowded Videos
* 2007: Feature-based Part Retrieval for Interactive 3D Reassembly
* 2007: Spatio-temporal Shape and Flow Correlation for Action Recognition
* 2008: Fast Motion Consistency through Matrix Quantization
* 2008: Learning class-specific affinities for image labelling
* 2008: Semi-Supervised Clustering via Learnt Codeword Distances
* 2008: Space-Time Shapelets for Action Recognition
* 2008: Unifying discriminative visual codebook generation with classifier training for object category recognition
* 2008: Volumetric Features for Video Event Detection
* 2009: PFID: Pittsburgh fast-food image dataset
* 2009: Trajectons: Action recognition through the motion analysis of tracked features
* 2010: Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval, A
* 2010: Food recognition using statistics of pairwise local features
* 2010: Motif Discovery and Feature Selection for CRF-based Activity Recognition
* 2010: Optimizing one-shot recognition with micro-set learning
* 2010: Representing Pairwise Spatial and Temporal Relations for Action Recognition
* 2010: Volumetric Features for Video Event Detection
* 2011: Fast and accurate global motion compensation
* 2011: Feature seeding for action recognition
* 2011: Large-Scale Multimedia Retrieval and Mining
* 2011: Localizing actions through sequential 2D video projections
* 2011: Measuring and reducing observational latency when recognizing actions
* 2011: probabilistic representation for efficient large scale visual recognition tasks, A
* 2011: Prop-free pointing detection in dynamic cluttered environments
* 2011: Violence Detection in Video Using Computer Vision Techniques
* 2012: Classification of plant structures from uncalibrated image sequences
* 2012: Classifier Ensemble Recommendation
* 2012: D-Nets: Beyond patch-based image descriptors
* 2012: Efficient Closed-Form Solution to Generalized Boundary Detection
* 2012: Importance-weighted label prediction for active learning with noisy annotations
* 2012: Model recommendation for action recognition
* 2012: Unsupervised Learning for Graph Matching
* 2012: Weakly Supervised Learning of Object Segmentations from Web-Scale Video
* 2013: CrowdCam: Instantaneous Navigation of Crowd Images Using Angled Graph
* 2013: Discriminative Segment Annotation in Weakly Labeled Video
* 2013: Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
* 2013: Spatiotemporal Deformable Part Models for Action Detection
* 2014: Classification of Cinematographic Shots Using Lie Algebra and its Application to Complex Event Recognition
* 2014: DaMN: Discriminative and Mutually Nearest: Exploiting Pairwise Category Proximity for Video Action Recognition
* 2014: Generalized Boundaries from Multiple Image Interpretations
* 2014: Large-Scale Video Classification with Convolutional Neural Networks
* 2014: Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts
* 2014: Video Object Discovery and Co-Segmentation with Extremely Weak Supervision
* 2015: Articulated motion discovery using pairs of trajectories
* 2015: MatchNet: Unifying feature and metric learning for patch-based matching
* 2015: Preface to 3D Reconstruction and Understanding with Video and Sound
* 2015: Robust video segment proposals with painless occlusion handling
* 2015: Virtues of Peer Pressure: A Simple Method for Discovering High-Value Mistakes, The
* 2016: Discovering the Physical Parts of an Articulated Object Class from Multiple Videos
* 2017: Behavior Discovery and Alignment of Articulated Object Classes from Unstructured Video
* 2017: Cognitive Mapping and Planning for Visual Navigation
* 2017: Deep Learning for Visual Understanding
* 2017: THUMOS challenge on action recognition for videos 'in the wild', The
* 2017: Video Object Discovery and Co-Segmentation with Extremely Weak Supervision
* 2018: 2nd YouTube-8M Large-Scale Video Understanding Challenge, The
* 2018: Actor-Centric Relation Network
* 2018: AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions
* 2018: Deep Learning for Visual Understanding: Part 2
* 2018: Rethinking the Faster R-CNN Architecture for Temporal Action Localization
* 2019: Relational Action Forecasting
* 2020: Cognitive Mapping and Planning for Visual Navigation
* 2020: D3D: Distilled 3D Networks for Video Action Recognition
* 2020: GHUM GHUML: Generative 3D Human Shape and Articulated Pose Models
* 2020: Speech2Action: Cross-Modal Supervision for Action Recognition
* 2020: Weakly Supervised 3d Human Pose and Shape Reconstruction with Normalizing Flows
* 2021: Depth distillation: unsupervised metric depth estimation for UAVs by finding consensus between kinematics, optical flow and deep learning
* 2021: Neural Descent for Visual 3D Human Pose and Shape
* 2021: THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers
* 2023: Self-supervised Hypergraphs for Learning Multiple World Interpretations
Includes: Sukthankar, R.[Rahul] Sukthankar, R.
89 for Sukthankar, R.
Sukthanker, R.S.[Rhea Sanjay]
* 2022: Generative Flows with Invertible Attentions